Why Should Your Business Consider Content Moderation?

User-generated content (UGC) is becoming an integral part of the decision-making process for customers when choosing a product or service. Almost half of the consumers think user-generated content is more trustworthy than any other form of media, and even 35% say it is more memorable than any other source.

Nonetheless, publishing such UGC can also negatively impact a brand’s reputation or defame it through offensive or inappropriate harmful content created by users. User-generated content should be moderated in order to protect your brand’s reputation, control invasive user-generated content, and prevent such content from spreading throughout your website.

Let’s find out why your company needs a content moderation service or why it is crucial to the success of your business.

Brand Protection with Care for Users

The sharing of UGC can take many forms, such as pictures, texts, or videos, via multiple online channels, such as social media, forums, blogs, comments, and contests. It is possible for many people to share negative UGC that damages your brand and influences your customers, which ultimately impacts the company’s corporate reputation.

It is important to manage content moderation efficiently and effectively in order to prevent your company’s brand from being damaged. Furthermore, these content moderators help to prevent trolls, bullies, and negative reviews posted by either existing or unknown customers that would damage your brand image.

Helps Understand Your Customer’s Sentiments

Content moderation helps you to understand your customer’s opinions, feelings, and thoughts toward your company, product, services, or brand. UGC helps to understand customers’ views and what they think about your product or service. And content moderation helps to interact with targeted customers and offer them better products and services.

Content moderators discover the actual pain or perception of customers that you don’t know. These moderators interact with such people and use sentiment analysis tools to get more comprehensive feedback to improve service and offer them a better product with value for the money. Content moderation with positive intents will help you protect your brand and users and make data-driven decisions before launching a new product and how to make crucial decisions for marketing campaigning.

Scale Your Campaigns with Rapid Actions

Content moderation is one of the best tools to scale your campaigns and drive marketing strategies if you are looking to host a contest or launch a new product or get more reviews, or can crowdsource a new idea with the help of an efficient content moderation strategy to rapidly scale such campaigns without getting any negative feedback or effects on your brand.

You can use social media platforms to analyze the UGC and reform the strategy to run the campaigns on Facebook, Twitter, Instagram, or other social networking channels. Using this tool, you can review the content posted on your website before any hateful content is exposed to your customer. The best part of this job is that moderators can work from different locations worldwide while increasing customer engagement.

Improves Online Traffic and Website Ranking

Suppose you are monitoring your brand or company’s official online pages. In that case, UGC will give the best method for driving more online traffic to your web pages, which also helps improve the website’s overall ranking. The increase in such online traffic will also generate customers’ interest in your brand, which helps to create a better brand image in the marketplace while increasing social engagement through your website.

UGC on social platforms will allow you to run a contest or solve any challenge to keep them more engaged, make the conversation more productive also encourage new users to participate and appreciate your brand, product, or services offered by your company. When new users engage more with your website or linked pages, search engines pay more attention to the content, increasing online traffic and significantly improving the overall website ranking.

Content Moderation Process

Each content moderation process can be executed by hiring individuals to check user posts or through AI-enabled automated methods. There are several factors to consider:

  • Type of online content and communities
  • Brand’s or business’ permissible or prohibited words, phrases, or visuals
  • A brand’s daily load of user posts

User content should be regulated in accordance with a guideline that specifies the scope and limitations of that regulation. A brand or client may prohibit it from using terms or phrases related to terrorism or implying sexual behavior. The moderation methods will be determined based on how followers and members of the online community post their comments.

In order to prevent content that should be banned or that requires further examination, each post is carefully reviewed by moderators. If user content is gravely offensive or violates the community guidelines, the moderators will delete it. With manual and AI-powered moderation methods, Cogito can aid in accurately inspecting user-generated content.

Content Moderation Services

For companies to build customer loyalty, content moderation is essential. It is more likely that customers who feel valued will be more open about their opinions regarding your products or services because they can trust that their opinions will be considered.

Content Categorization for Personalization

Customer satisfaction can be increased by personalizing the experience and protecting them from harmful content. Moderators are also responsible for categorizing content based on specific demographics, interests, and market behavior through content moderation.

A better online experience is made possible with content categorization. An ad for baby shampoo, for instance, would target women with babies and toddlers ages 25-40. Besides personalizing it, they want to target moms looking for the best baby shampoo brands. Content categorization ensures the right audience sees the right ads.

Content Moderation for Market Intelligence

You can also use content moderation to gather information from user-generated content, like opinions, reviews, and comments about your product or service. There is an increasing number of businesses utilizing this opportunity to gather market intelligence that can be used to develop better product and marketing strategies, to develop better offers, and, ultimately, to develop better products and services.

Decisions made by potential customers are heavily influenced by brand perception. Online reviews, friends’ experiences, or news stories mentioning a specific brand influence these potential customers’ opinions quickly about brands. It’s important to keep track of what others are saying about your brand so you can better understand what makes you distinctive in the competitive corporate or business space.

Social Media Monitoring

Social media has become a necessity for every brand today. Users post and comment on your social media pages. Generally, the social media content norms govern most of them; however, some can contain inaccurate and hateful content. It could also contain videos and photos outside the platform’s policies.

In order to maintain a positive social media environment, content moderation services manually review and moderate all user-generated content on your various social media pages.

Comment Moderation

The more comments your website visitors leave about your articles, products, or services, the better. It makes prospects feel heard and valued. Some of the comments, however, may be spam or not in line with the content submission guidelines.

You can moderate comments on your site or forum by outsourcing content moderation. To ensure only relevant content appears on your website, follow the strict guidelines you’ve laid out.

Video moderation

Videos dominate the internet these days, so your website must not post pornographic, violent, or obscene videos. Keeping a high level of quality and compliance is imperative in today’s business environment.

It’s easy to monitor thousands of videos, categorize them accordingly, and disapprove any that don’t meet guidelines with an outsourced team of moderators.

Image moderation

New users are attracted to a site through imagery, which plays a major role online. Online interactions can be boosted by inviting users to share images. Cogito moderators ensure that no explicit content or images are shared on the platform by regularly watching users’ posts. The image moderation service ensures that every image meets your standards for quality and compliance.

Categorizing & Indexing

Make your website easier to search, easier to navigate, and more conversion-friendly by categorizing your business offerings. Businesses organize their products and services according to appropriate classifications to improve the efficiency of their indexing. Your message will be conveyed to your customers through cataloging services.

Product Data Entry

Providing proper search functionality to your website or app will make it more user-friendly from the first moment your customers engage. You can enhance your website/app customer experience by providing them with all essential information, including product specifications and tagging offerings with optimized keywords and “related items” listings.


User-generated content plays a crucial role in understanding the customers and their state of mind. And content moderators can monitor UGC to make it favorable towards a particular brand or company. If you hire a professional service provider offering content moderation services, you can establish a reputed brand image for your company.

Cogito is the right place where you can meet the requirement for spam content detection services with effective implementation and solutions. Cogito has expert content moderators to provide data-driven social media content moderation services with a completely customized solution with accuracy and dedicated resources within your budget.

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5 Key Business Applications of Sentiment Analysis

In order to gauge whether your customers are happy with what you’re doing, customer satisfaction is crucial. It is proven that a high degree of satisfaction increases the lifetime value of a customer, more customer retention, and a stronger reputation of the brand. Alternatively referred to as “opinion mining,” sentiment analysis can help product managers understand the customer satisfaction level.

Analyzing customer sentiments to predict accurate sentiments has been challenging and time-consuming due to the high volumes of collected data from various sources. Having an NLP and AI expert on hand can help you get started with sentiment analysis if you want to understand your customers in a dynamic market.

What Goes into Procuring Sentiment Analysis

Artificial Intelligence algorithms, Natural Language Processing (NLP) methods, and Machine Learning (ML) models contribute to sentiment analysis. This includes neural networks, unigram algorithms, skip-gram algorithms, and bag-of-word regression models using machine learning and deep learning (DL). The use of natural language processing techniques in machine learning software allows unstructured texts to be sorted by emotion and opinion.

As an example, let’s look at this. Imagine that you are responsible for understanding the general sentiment in the support conversations at your company. Get a sense of your customers’ ongoing mood and tone. You would need an emotion classifier in such a situation to produce predictions regarding relevant conversations. Dashboards that are often designed to measure customer satisfaction can be populated with sentiment data you’ve collected from your customers.

Why is Sentiment Analysis Important in Business?

At first glance, it may seem like a fancy class project, but sentiment analysis has many practical applications in the business world. Using sentiment analysis to solve business problems is an excellent starting point.

  1. You can aggregate customer sentiment from free-form feedback data and identify the primary promoters or detractors of your product or service. Once the detractors have been converted to promoters, you can gradually implement corrective measures to rebuild trust.
  2. It is possible to detect hateful and inappropriate comments on your online platform, which will help you keep it free of bullies.
  3. The feedback you receive from recent employees, peer reviews, and manager feedback can help you identify demotivated or about-to-quit-employees and provide them with constructive ways to succeed.

Based on these sentiment analysis examples, you can see that sentiment analysis is an excellent tool for gaining a better understanding of employees and customers, keeping platforms safe, improving customer shopping, and learning from competitors. Sentiment analysis can be combined with other artificial intelligence technologies like text summarization to provide deeper, more powerful insights.

Industry Applications of Sentiment Analysis

In sentiment analysis, the text is analyzed to identify and extract subjective information, which businesses can use to measure how customers feel about their service offerings. Using AI-driven technology, sentiment analysis traces the undertone of text by channeling extensive digital information.

The use of sentiment analysis is widespread across various industries. Several popular sentiment analysis applications exist, such as monitoring social media, managing customer support, and reviewing customer feedback. Here are a few more to get you going with the idea of integrating sentiment analysis into the marketing & customer analysis symmetry of your business:

  1. Social Media Monitoring

Social media posts often contain some of the most honest opinions about your products, services, and businesses because they’re unsolicited. By incorporating NLP experts on board, you can analyze individual emotions and overall public sentiment across all social platforms based on their comments, feedback, and reviews.

In chat, sentiment analysis can detect sarcasm, understand acronyms (OMG, LOL, ROFL, etc.), correct misspellings and misuses, and detect sarcasm.

  1. Managing & Monitoring Business Reputation

Sentiment analysis is widely used in business for brand monitoring. A business can leave negative reviews online for a long time, and the longer it does so, the worse the situation will become. Negative brand mentions can be detected as soon as they occur with sentiment analysis tools.

Furthermore, you can monitor your progress over time by keeping track of your brand’s image and reputation. You can use this information to develop usable statistics and information about your brand, whether you are monitoring news stories, blogs, forums, or social media for information about your brand.

In addition to following trends and anticipating outcomes, machine learning can be relied upon to stay one step ahead of the competition and become proactive rather than reactive.

  1. Analyzing the Market Response to a Product

Learn what users are saying about your new product soon after launch, or examine feedback you may have yet to see. Aspect-based sentiment analysis allows you to locate only the information you need based on your search terms (interface, UX, functionality). Identify what makes your most valuable customers happy, learn how your product is perceived by your target audience, and improve your product. The whole thing is based on sentiment analysis.

  1. Get to Know the Voice of Your Customers (VoCs)

Creating a personalized customer experience requires communication and listening to the voice of your customers. As a result, businesses have access to a large number of conversation sentiment analysis datasets that can be integrated into the development of ML models in order to automate the sentiment analysis process. Taking all feedback, you receive from customers, such as web feedback, surveys, chats, and customer service calls, and evaluating it. This data can be categorized and structured with the help of sentiment analysis so that you can discover recurring topics and concerns.

  1. Analyzing Market & Competitors

In order to research the market and your competitors, you can use sentiment analysis. Get a sense of who your competitors are mentioning positively and how your marketing efforts compare. Look at how your competitors speak positively to their customers, and take some of those phrases and use them in your own brand message and tone of voice guide.

Advantage of Manual Sentiment Analysis Over AI-Integrated Machines & Apps

In a world of billions of people, each communicates uniquely, e.g., adding tiny variations to the language and attaching sentiments to it, which are easy for us to understand but difficult for machines to understand. Sarcasm, for instance, is the opposite of what is true when we use it in our sentences. Sentiment analysis through machine learning models is difficult to predict customer behavior, as a matter of fact. Therefore, manual sentiment analysis has an advantage over automated sentiment analysis performed through AI-integrated applications and machines.

Final Words

For accurate sentiment analysis results, a blended approach to sentiment analysis is the most effective way for businesses to utilize it to understand customer behavior. Consequently, Cogito, an NLP expert, combines manual and automated approaches for accurate sentiment analysis.

In addition to manually conducting sentiment analysis of your social media channels and customer feedback on other platforms, the experts here can also assist you in getting AI training data in order to build ML models for automated sentiment analysis based on real data rather than assumptions made based on a small sample.

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Insightful Interpretation of Machine Learning Datasets

It is possible to simulate human intelligence in machines with artificial intelligence (AI) and machine learning (ML). These simulations allow them to complete a variety of tasks without much human assistance – Companies need precise training data if they are to develop AI and ML models that are more efficient and newer. It is possible to gain a better understanding of a given problem through the use of training datasets which can subsequently be enriched through data annotation and labelling for further use as artificial intelligence (AI) training data.  

What is Machine Learning?

The goal of machine learning is to imitate humans’ learning process through the use of data and algorithms. It gradually improves the accuracy of its predictions. Statistical methods allow algorithms to be trained to make classifications or predictions within data mining projects using machine learning — this provides key insights into the data. 

Ideally, data mining improves business and application decision-making, influencing key growth metrics through these insights. Increasing demand for data scientists will result from the continued growth and development of big data, which requires them to identify the most pertinent business questions and the data that will be required to answer the questions.

Types of Machine Learning

An algorithm learns to improve its accuracy by applying supervised, unsupervised, semi-supervised, and reinforcement learning approaches. These four basic approaches are classified according to how an algorithm learns. Data scientists choose which algorithm and machine learning type depending on the data they wish to analyze.

  1. Supervised Learning 

These types of machine learning algorithms require labeled training data and variables data scientists want the algorithm to evaluate for correlations. Here, the input and output of the algorithm are both specified by the data scientists.

  1. Unsupervised Learning

It involves algorithms that learn from unlabeled data, where an algorithm scans data sets to identify meaningful connections. All predictions or recommendations are predetermined by the data that the algorithms train on.

  1. Semi-supervised Learning

There are two approaches to machine learning in this approach, the model is fed mostly labeled training data by a data scientist, but it is free to explore the data on its own and develop its own insights about it.

  1. Reinforcement Learning

As part of reinforcement learning, data scientists teach a machine how to complete a multistep process governed by clearly defined rules. For the most part, an algorithm decides how to complete a task on its own, but data scientists program it to complete it and give it positive or negative cues as it works out how to accomplish it.

Real-world Machine Learning Use Cases

You might encounter machine learning every day in the following ways:

  1. Speech Recognition 

Alternatively called automatic speech recognition (ASR), computer speech recognition, or speech-to-text, this technology converts human speech into the written form using natural language processing (NLP). A number of mobile devices include speech recognition in their systems so that users can conduct voice searches—like Google Assistant in Android smartphones, Siri in Apple devices, and Amazon’s Alexa in media devices. 

  1. Customer Service

Human agents are being replaced by online chatbots as customer service grows. We are seeing the shift in customer engagement across websites and social media platforms as these companies provide answers to frequently asked questions (FAQs) around topics such as shipping or product delivery, or cross-selling product recommendations. Slack and Messenger, for example, as well as virtual agents and voice assistants, are some examples of messaging bots on e-commerce sites with virtual agents.

3. Computer Vision

Computers and systems can use this AI technology to glean meaningful information from images, videos, and other visual inputs; Using this technology, they can take action based on these inputs. It is distinguished from image recognition tasks by its ability to provide recommendations. The application of computer vision in the industry of photo tagging on social media, radiology imaging in healthcare, and self-driving cars is based on convolutional neural networks.

  1. Recommendation Engines

Online retailers can make useful add-on recommendations to customers during checkout using data on past consumption behavior. AI algorithms can help us discover data trends for developing more effective cross-selling strategies.

  1. Automated Stock Trading

Without human intervention, AI-driven high-frequency trading platforms execute thousands or millions of trades every day in order to optimize stock portfolios. 

What is Training Data?

Machine learning algorithms develop an understanding of datasets by processing data and finding connections. In order to make this connection and find patterns in processed data, an ML system must first learn. After the ‘learning,’ it can then make decisions based on the learned patterns. ML algorithms can solve problems from retro observations – Exposing machines to relevant data over time allows them to evolve and improve. The training data quality directly influences the ML model’s performance quality. 

Cogito is a leading data annotation company assisting AI and machine learning enterprises with high-quality training data. In its decade-long journey as a data procurer, the company has built credibility for the accuracy and timely delivery of training data to ensure the quick accomplishment of data-driven AI models. 

What is Test Data?

When an ML model is built using training data, you need to test it with ‘unseen’ data. This testing data is used to evaluate the future predictions or classifications the model makes. The validation set is another partition of the dataset that is tested iteratively before the test data is entered; this testing allows developers to identify and correct overfitting before the test data is entered. 

Both positive and negative tests are performed using test data to verify functions produce the expected results for given inputs and to determine whether the software is capable of handling unusual, exceptional, or unexpected inputs. As your test data management strategy can be optimized by outsourcing data annotation to an industry expert, you can ensure quality information reaches test cases more quickly.

Training Dataset vs. Test Dataset

An ML model can learn patterns by learning insights from training data, which is approximately 80% of the complete dataset to be fed into the model. Testing data represent the actual dataset since they evaluate the model’s performance, monitor its progress, and skew it for optimal results.

The training data is typically 20% of the entire dataset, while the testing data confirms the model’s functionality. In essence, the training data train the model, and the testing data confirms its effectiveness. 

Enriching Datasets Using Data Annotation & Labeling

Building and training an ML model will require large volumes of training data. Data annotation is the process of adding tags and labels to training data. In order to achieve this goal, ML models require properly annotated training data in order to process data and gain specific information. 

Data annotation helps machines identify specific patterns and trends in data by connecting all the dots. Enterprises must understand how different factors affect the decision-making process in order to achieve business success. Data annotation services hold the key to accelerating businesses into the future. 

Cogito can Help with Data Annotation Services

With Live Enterprise, organizations can make intuitive decisions automatically at scale, get actionable insights from real-time solutions, experience anytime/anywhere, and get deep visibility into data across functions to become more productive with AI and machine learning innovations. Cogito offers training data annotation services for machine learning and artificial intelligence. The agile system at Cogito combines human-empowered data annotation and automated annotation & labelling tools to process unstructured data. 

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Training Data to Employ AI in Healthcare

As artificial intelligence (AI) becomes an increasingly important tool in health care, it offers unprecedented opportunities for improving patient outcomes, reducing costs, and impacting population health. There are many examples, including automation, delivering a simple synthesis of complex health information to patients, families, and caregivers, and providing recommendations and visualizations for shared decision-making among patients, family members, and health professionals.

Need for AI Technology Adoption in Healthcare

As healthcare organizations realize the benefits of technology adoption as patient numbers rise and quality and affordable treatment become more important, they are adopting artificial intelligence. It is still early in the adoption of artificial intelligence in healthcare. The reason behind this is that many emerging economies lack the means of digitizing patient records. 

The increased number of diseases and the need for better understanding and diagnosis with the help of deep learning are some of the reasons for AI adoption in healthcare. In many healthcare organizations, AI has enabled them to treat more patients at lower costs and with improved efficiency. 

Need for Building Database for Healthcare AI

To effectively utilize AI in the healthcare industry, there is a need to build a database with all the historical data of patients, required to identify the patterns and accordingly use AI for diagnosis and therapy. If customized accurately, AI would not only be ‘intelligent’ but also highly user-friendly. 

It is also necessary to maintain records physically in certain countries in South Asia and Africa. However, the adoption of AI is relatively less challenging in developed countries such as the US and the UK, where the majority of healthcare data have already been digitized, and the technology has flourished. 

Significance of Data for AI Utilization in Healthcare 

In healthcare, data can be related to health conditions, reproductive outcomes, quality of life, and many similar life events determining an individual or an entire population. Health data includes clinical metrics along with environmental, socioeconomic, and behavioural information pertinent to health and wellness.

After getting to know the most important terms, definitions, and methods used in the field of artificial intelligence, we need to be familiarized with the current situation of data and data analytics in healthcare in order to be able to find out where artificial intelligence can come into play in healthcare; why and how we could make use of machine learning and smart algorithms – as the most important ‘base material’ for smart algorithms is data.

AI technologies that can Transform Healthcare

AI is based on technologies such as Natural Language Processing, Deep Learning, and Context-Aware Processing. The healthcare industry is undergoing a transformation as a result of these technologies. 

  1. Natural Language Processing (NLP) 

In today’s world of complex data, AI plays a significant role in simplifying insights, and NLP makes the process more accessible to more people. The goal of Natural Language Processing (NLP) is to mimic the responses of humans when answering queries and holding conversations by utilizing algorithms. Using NLP healthcare providers can summarize narrative text by highlighting key concepts or phrases, such as those found in long journal articles or clinical notes. 

Data elements in EHRs, which are represented as unstructured text, can be transformed into structured, meaningful data by using Natural Language Processing. Using NLP, Intermountain Healthcare, for example, has identified the causes of illness in patients by mining their medical records in order to identify cases like strokes, cancer, heart failure, and venous thromboembolisms (blood clots in the leg’s deep veins).

  1. Deep Learning 

By implementing AI in healthcare, diseases can be diagnosed and treated more efficiently. Medical data and images can be analyzed using deep learning, a component of AI, to enhance the ability of doctors to treat diseases. Visually challenged individuals can benefit from deep learning by narrating the text, identifying the facial expressions of nearby people, studying the surroundings, and describing the environment using computer vision, text-to-speech, and computer vision. 

Advanced neural network algorithms, GPUs that can process large amounts of data, and access to enormous amounts of internet data are all driving the deep learning revolution. A cognitive computing & deep learning approach is being used to train IBM Watson, for example, to assist doctors with medical diagnosis.

  1. Context-Aware Processing 

As examples of AI applications that can be used in healthcare, Apple’s Siri, Amazon Alexa, Google Assistant, and Microsoft Cortana can provide assistance based on the instructions provided by the programmer. Using AI chatbots in healthcare can eliminate the burden on medical professionals and reduce the time and cost of coordinating care and detecting issues or diagnosable health issues. 

Bots could be considered the next step in the transformation of health assistants. HealthTap, for instance, or Your. Md are Bots that use AI to diagnose the most common symptoms. Nevertheless, chatbots play an important role in directing patients to the right doctor for diagnosis and treatment. Doctors with experience would be able to rely on them to supplement their duties. 

  1. Intelligent Robotics 

There is also the possibility of using AI with robotics. Healthcare facilities can be revolutionized with physical robots that can help people stay healthy and reduce the number of hospitalizations. In addition to AI, advanced humanoid designs are enabling robots to converse with and socially interact with elderly patients. With robots, surgery can be performed with more precision and smaller incisions since their flexibility and reach are greater. 

Certain robots can serve as social partners to treat mental health issues or alleviate loneliness. Blue Frog Robotics (developers of BUDDY), National Institute of Advanced Industrial Science and Technology, and AIST (developers of PARO) are among the companies that have expanded companion robots into healthcare.

Many technology companies are currently conceptualizing AI-powered surgical robots by leveraging the capabilities of machine learning applications like Google DeepMind, IBM Watson, and others. With AI-enabled robots, less damage will occur, precision will increase, and recovery will be faster.


With the growing amount of digital data and the complication of overseeing a large number of patient records, all end-users under the healthcare umbrella are embracing artificial intelligence. AI-enabled content analytics can enhance providers’ ability to administer effective and efficient treatment protocols by speeding up the diagnosis of patients’ conditions through the use of NLP tools.

Although AI has many proven applications in the healthcare sector, the extent of its implementation in medical practice is still at an initial stage. The healthcare industry is expected to undergo a transformational change as AI adoption increases. It is estimated that healthcare data diagnosis and treatment procedures will improve as a result of the expected growth in the future. The healthcare industry can reap numerous benefits from AI, which has a wide range of applications.

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Sentiment Analysis: Types, How it Works & Why it is Difficult?

Understanding the sentiments of the people is not easy unless they express their feelings, opinions and perspective anything. But if you have such platforms where people are freely speaking up about their thoughts and concerns, you can easily find out their sentiments. Here where Cogito comes in the facility of sentiment analysis.

Sentiment Analysis is the process of determining the conceptions, judgments, feelings, opinions, viewpoints, conclusions, and other notions towards anything. It is a technique to analyze texts, images, emojis and various other actions to know what other people think about a product, service, company, brand name, or a reaction to a specific event, social movement, etc.

The Usefulness of Sentiment Analysis

Sentiment analysis is playing an enormous role in understanding people belonging to different groups and their sentiments. On political grounds, it helps to know how much of the majority is in favor of the Govt. or how many stands opposing to their services and measures undertaken.

While on the other hand, in the business world, it is helping companies to know their customers in a better way. Such a resource becomes useful for the business enterprises to offer products and services as per the expectations of their potential customers and get appropriate results.

Social Media is one the best and biggest platforms where the theory of sentiment analysis is and must be applied, to interpret the feelings of various people. Hence, we need to understand it as a process, how it works, its applications, and why it is important for business organizations and other aspects.

What is Sentiment Analysis in NLP?

Natural Language Processing (NLP) is a way to understand the actual meanings of the text words, sentences, or entire written documents. NLP is used to train the machines thereby, helping them understand the language and communication process among humans while discussing a topic.

The main motive of sentiment analysis is to find out expressions of people that are eventually classified as positive, negative, or neutral. It can be used in diverse areas such as company products, market research, marketing analysis, customer targeting, product reviews, customer feedback, reputation management, etc.

How Does Sentiment Analysis Work?

As mentioned above, sentiment analysis is used in NLP-based Machine Learning algorithms to develop such AI applications that can understand the ways of linguistic context showing the sentiments of different people.

But the question here is, how does sentiment analysis work? The developers begin by creating a text Machine Learning-based algorithm that can detect the contents showing any specific sentiment indicator.

Afterward, they train the ML classifier by feeding it a huge quantity of training datasets containing reactions based on positive, negative, and neutral sentiments. Every piece of content is scattered and divided into basic components such as text words, phrases, sentences, and other entities.

Once this process is completed, the relationship between the topics and the identification process commences. Then, AI model assigns a sentiment score to that particular post. The post can range from 1 representing negative and +4 representing 4 positive comments. If sentiment is neutral, the score is usually given 0.

As we already know, understanding the different human languages is a very complex task due to their cultural and social diversity. Hence, it is important to train the programs in such a way that they are able to detect and analyze grammatical nuances.


To understand the sentiments of people, there are different types of sentiment analysis used in the market. Apart from normal opinions – positive, negative, or neutral, other types of sentiment analysis help in understanding people’s inner feelings, their actual intentions, and emotions.

Fine-grained Sentiment

This is one of the most simple and common ways of understanding your customers’ sentiments. Yes, fine-grained sentiment analysis helps in studying the ratings and reviews given by the customers.

While analyzing the sentiments, you can use the readily available categories like very positive, positive, neutral, negative, or very negative. Providing a rating option from 1 to 5 is yet another way to scale the feedback given by your customers. Most e-commerce sites use this technique to know the sentiments of their customers.

Aspect-based Analysis

This type of sentiment analysis is more focused on the aspects of a particular product or service. To make it easier to understand, let’s take an example – if you are talking about a soundbar or a wireless speaker system.

Here you can analyze your customer’s sentiments by asking them for feedback about the sound quality, connectivity, and other features, making such devices more useful and productive for the users. It helps in determining specific attributes of the product.

Intent-based Sentiment Analysis

To know the intent of the customers; whether they are looking to buy the product or just browsing around, is achievable through intent analysis. It not only helps to identify the intent of the customers but also to track and target them through advertisements or other ways of online promotions.

With intent analysis, companies can save their time, efforts, and cost while targeting the potential customers as per their intentions. This helps in getting a more vivid understanding of the intentions of the customers.

Emotion Detection Sentiment Analysis

As the name symbolizes, this type of sentiment analysis helps to detect and understand the emotions of the people. Emotions like anger, sadness, happiness, frustration, fear, panic, worry, or anxiety, may all be included.

Understanding the sentiments of people using emotion detection is even more difficult as people use a collection of words having a different senses of meanings. Hence, inaccurate emotion detection can lead to an inaccurate decision while analyzing the true sentiments of the people.

How Can Sentiment Analysis be Used?

The main motive of using sentiment analysis is to find out the true feelings of the varied people living in our society. It can be used for analyzing the customer’s feedback of a particular company, normal users on social media towards a product, service, social issues, or political agendas.

Companies also use it for brand analysis, reputation crises, campaigns performances, competitor analysis, and improve the service offered to the customers. Analyzing the sentiments of the customers helps the customer support team to prioritize their work for offering better service to end-users.

Why Sentiment Analysis is Difficult?

Sentiment analysis is a very difficult task due to sarcasm. The words or text data implied in a sarcastic sentence come with a different sense of meaning depending on the sender or situation.

Sarcasm is remarking someone opposite of what you want to say. It is expressed to hurt someone’s feelings or humorously criticize something. On social media, sarcasm is one of the most common behavior you can see nowadays interfering with the results.

Sarcastic words or texts show the unique behavior of people. It is usually used to convey a negative sentiment using the positive intention of words. This kind of caustic remark can easily mislead the sentiment analysis decisions.

The presence of sarcastic words makes it difficult for sentiment analysis processing in turn making it difficult to develop NLP-based AI models. Hence, a deeper analysis of such words is required to understand the true sentiments of people with accuracy.

In such a case, we can use the psychographic-based analysis to understand such people and their exact intention of what they are trying to say. Using psychographic segmentation in sentiment analysis can give a more comprehensive perception of different kinds of people interacting with each other.

Sentiment Analysis using Psychographic Segmentation

Sentiment analysis can be a big game changer in forming a more focused marketing strategy for the companies. But these establishments need to understand their customers by segmenting them into their characteristics like their cultural values, beliefs, desires, goals, interests, and lifestyle choices.

Psychographic Segmentation becomes helpful here in analyzing the customers’ sentiments by segmenting them based on their activities, lifestyle, and interests. It is a more qualitative approach to studying consumers according to their psychological characteristics.

Psychographic segmentation not only helps in improving the customer experience but also authorizes companies to offer more tailored products or services to the right customers at the right time improving the return on investment.

Psychographics allows you to learn about the deeper motivations and emotions that influence potential customers. Hence, in the next article, we will learn more about psychographic segmentation, and how it is helpful in sentiment analysis.

Cogito offers sentiment analysis services for wide-ranging industries using the insights of our skilled workforce making every interpretation meaningful. From social media to other useful online platforms, we are experts in analyzing the sentiments of different groups of people towards different entities.

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Social Media Sentiment Analysis Using Twitter Datasets

Several hundreds of thousands of raw data files are uploaded by users every day to social media sites. Online user data provides access to an enormous amount of information regarding products, services, places, and events, which makes it suitable for sentiment analysis. Valuable information can be extracted by analyzing the sentiment of the data.

It is a method for interpreting opinions within a text that uses Natural Language Processing (NLP) to extract positive, negative, and natural meanings from user-generated content shared on social media platforms. Sentiment analysis has been previously applied to products or movie reviews to understand customers’ interests better and, thus, improve outcomes and service offerings. Sentiment Analysis Types/Levels & Methods

Experts define sentiment analysis with three categories/levels, i.e., Sentence level, Document level, and Feature level. This exercise classifies positive and negative sentiments from a sentence, document, or feature.

It has been determined that two main sentiment analysis methods exist: machine-learning techniques and lexicon-based techniques. The machine learning approach focuses on extracting and detecting sentiments from data, while the lexicon-based approach counts positive and negative words that appear in the data.

Social Media/Networking Sites and Types based on Content Sharing

There are many social networking sites for users to share their minds, life stories, narratives, and opinions online. There are four different types of social media sites, based on what types of content users are allowed to share online:

  • Content communities (Youtube, Instagram);
  • Social networking (Facebook, LinkedIn),
  • Blogs (Reddit, Quora);
  • Micro-blogging (Twitter, Tumblr).

Microblogging sites are the most common social media sites to extract information for sentiment analysis. These micro-blogging social sites, particularly the likes of Twitter, are found to be the best for gathering insights from user-generated posts and opinions shared online.

The online ecosystem of social media platforms is getting more mature as time goes by. Social media platforms are turning out to become an ocean of valuable information. Each social media content might be different, and it is worth exploring the right social media platform to source or extract users’ information. However, bloggers, WordPress, YouTube, and other social media sources are given much less attention when extracting insights from users’ opinions shared online.

The most popular social media site to extract information is Twitter. Most of the reviewed papers use Twitter as their social media context. This is due to the availability, accessibility, and richness of Twitter content. Twitter enables users to post and interact with short messages allowing them to express opinions. There are millions of tweets every day on almost any topic. The available content or data for public use makes Twitter popular and the best social network for sentiment analysis.

Twitter — Why It’s Best for Sentiment Analysis

The user-generated content on Twitter can provide valuable information to scholars, business organizations, and the government. Twitter conducts real-time analysis and closely public sentiment as Twitter has about 500 million tweets per day, and it allows public access to its data through API. Twitter is used to search and collect tweets from 8 different countries from western and eastern countries. There is Twitter user all around the world, thus making it rich with opinion and views by people from other countries, languages, and perceptions.

Application of Sentiment Analysis

Application context of sentiment analysis The application of sentiment analysis ranges from business and marketing, politic, and health to public action. Sentiment analysis is not limited to one application but provides a vast application in different areas to assist decision-making. Sentiment analysis can be applied to events, activities, sports, or worldwide disasters.

Here are some significant applications of sentiment analysis to look into. Sentiment analysis can:

  1. raise awareness of data security and the danger of security breaches;
  2. acts as a guideline for companies to respond to security breaches in shaping public perception;
  3. Identify people’s sentiment needs during a disaster and prepare an appropriate rescue & response plan
  4. Help find the level of depression of a person by overserving and analyzing emotions from the text.

Furthermore, sentiment analysis was conducted on social media’s unemployment rate and employment sentiment score. We can see the application of sentiment analysis in healthcare and where the study uses Sentiment analysis as a service framework proposed and utilize Spatio-temporal properties to identify locations of disease outbreaks. Political elections can be predicted with sentiment analysis based on Twitter data. There is a 94% correlation between Twitter data and polling data, which indicates that users’ opinions on the social sharing platform are more reliable than polling data. Twitter may therefore be an even more reliable polling method than sophisticated methods.

Choosing the Right Social Media for Sentiment Analysis

Facebook boasts of being the largest social sharing platform by user numbers. But sentiment analysis through Facebook is not feasible as the data here is too messy to extract. The user-generated content on the platform needs to be structured better.

Users often commit spelling blunders and errors in sentence framing while posting content on Facebook, making the data hard to extract any context. The case is the same with other significant content and social sharing platforms. This puts microblogging/micro-sharing sites in a better place as the best choice for sentiment analysis.

Studies show that 88% of the data comes from Twitter. The other source of social media is not preferable because the number of data or opinions that can be extracted is limited, such as in Blogspot, YouTube, and WordPress.

How Social Media Sentiment Analysis Makes a Business Impact

Businesses gain significant advantages from sentiment analysis by identifying how customers perceive their products or service—assessing the effectiveness and capability of social media and business brand communication and analyzing the flow of businesses and stock prices.

It is displayed by a study comparing sentiment data on Big Six consumer tweets (Britain’s largest and oldest gas and electricity supplier and new entrant energy consumer). The result indicates that the sentiment from the Big six is more negative than a new entrant energy consumer. In addition, sentiment analysis on social media allows the organization to evaluate the success level of a program, as shown in a study where a high positive sentiment is obtained from a tweet on community development program activity. The result can help improve the community’s overall living standard.


There are many applications for sentiment analysis, including improving quality and strategy within a business, forecasting elections, monitoring disease outbreaks, increasing awareness of the importance of data security, improving perception towards a particular sport, and locating and responding to disasters more effectively.

Now that sentiment analysis helps us understand people’s perceptions and make decisions, we have realized how important it is. However, developing a universal sentiment analysis model can be applied to a wide range of data types. Exploring other social networking sites to obtain user opinions and expanding the context of sentiment analysis applications are essential for future recommendations.

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Incorporating Sentiment Analysis into E-commerce

Studying users’ behavior and understanding their sentiments have become substantial to businesses with the increasing platforms operating in the digital space and users’ traffic rapidly growing on these online platforms. The Internet replaced sources such as friends, relatives, and consumer reports which used to be the primary sources of opinions on products and services. As a method of determining users’ opinions and subjectivity, sentiment analysis is an emerging new source.

An item’s online reputation is simply the opinion of its internet community — buyers and users. Since psychological scrutiny of buyers’ sentiments about a specific product can drive motivation in prospective buyers in order to influence their buying decision, analyzing their sentiments can be critical to determining an e-commerce business’ future trajectory.

Buyers’ Sentiments are Crucial

When a buyer decides to make a purchase from an online store, he prefers to have a peek into other buyers’ views about the item, just to be sure that the product he is spending his pretty penny on is worth grabbing or not. The sentiments of buyers about a product, whether negative or positive, revealed in the ratings and reviews give prospective buyers a sense of acumen to decide to buy the same product. It, thus, becomes of utmost importance for an online platform to incorporate sentiment analysis into e-commerce practice.

What Sentiment Analysis Can Reveal

People’s sentiments about a product can be revealed by sentiment analysis. As a result, sentiment analysis can first be used to provide indications and recommendations on what products to choose based on the wisdom of the mass. Generally, certain aspects of a product appeal to you when choosing it to buy. Ratings based on global averages could be misleading. Based on sentiment analysis, certain aspects of the product can be rated based on the reviewers’ opinions.

Another utility of sentiment analysis is for companies that want customers’ opinions on their products. They can then improve the aspects that the customers found unsatisfying. Sentiment analysis can also determine which aspects are more important for the customers.

Sentiment Classification

It is possible to conduct sentiment analysis at three different levels: document level, sentence level, and aspect/feature level, which is then followed by aspect extraction and sentiment classification.

Document Level Classification

The overall sentiment of the opinion holder is analyzed in this process, and a whole opinion is classified according to it. In order to classify a review, one must determine if it is positive, negative, or neutral.

“Although a little large, this phone is so nice. I like the touch screen. The voice quality is clear. I just love it!”

Is the review positive or negative? A document-level classification is most effective when it is written by a single person and expresses a single opinion or sentiment.

Sentence Level Classification

The sentence level classification includes two classifications based on subjectivity and sentiments:

  1. Subjectivity classification leads to be objective and subjective nature
  2. Sentiment classification tends to be of positive and negative nature

Subjective sentences convey feelings, emotions, and beliefs, whereas objective sentences present facts. Subjective sentences can be identified in various ways, such as using Naïve Bayesian classification. In order to understand what a user’s statement refers to, we need to know if it is positive or negative. The sole objective of it is to filter out sentences with no opinion and to determine to a certain extent whether sentiments are positive or negative about entities, products, or services. A subjective sentence may include multiple opinions and factual and subjective clauses.

Aspect/Feature Level Classification

This process aims to determine whether the opinion holder’s comment on an object feature is positive, negative, or neutral by identifying and extracting its features. We group features based on their synonyms and create a feature-based summary based on multiple reviews.

Aspect Extraction

It is necessary to find all adjectives that frequently occur across reviews in order to identify all the aspect terms (e.g., great food) that appear in a sentence. This can be followed by building a list of phrases that occur frequently. Alternatively, you can search the reviews for all aspects. For instance, a restaurant’s food, service, value, and décor could be its aspects.

Sentiment Classification

Positive and negative sentiments, strong or weak, are expressed through words. When performing sentiment analysis, it is important to distinguish positive and negative sentiments. Sentiment lexicons can be used for this purpose. A sentiment lexicon categorizes words according to their positive or negative sentiment. The words with negative connotations used in a sentence in customer reviews can be considered sentiments.

How Sentiment Analysis can Help Businesses Gain Accurate Consumer Insight

Organizations use different channels, e.g., social media, online forums, surveys, and online opinion polls, to get an insight into the feedback they receive from buyers for their products and services. However, the computational study of how a product or service is doing in the market and how customers are responding to it in terms of purchase can bring a huge margin of profit to a business. Sentiment analysis can significantly help an e-commerce business set the right path for product or service improvement based on consumer feedback.


Sentiment analysis has evolved with the evolution of the internet, particularly with the emergence of e-commerce platforms. Since sentiment analysis tasks are challenging due to the complexity of the sentiment analysis datasets sourcing and their natural language processing (NLP) origin, expert intervention is necessary. Cogito, an NLP expert, has been in the industry for over a decade and can bring you experts to perform sentiment analysis around your e-commerce businesses to derive accurate customer insight for products and service offerings.

The growing need for product insights – and the technical challenges currently facing the field will keep sentiment analysis and opinion mining relevant for the foreseeable future in the e-commerce industry.

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Why radiologists in the healthcare sector will gain from AI

In radiology field, talk of artificial intelligence (AI) has become commonplace. Many think AI, also known as machine learning or deep learning, may improve radiologists’ operations, enable quantitative radiology, and help identify genetic markers.

Radiologists are very active medical specialists. Any errors would be unaffordable. They must communicate with a broad spectrum of referring medical professionals, including neurologists, urologists, orthopedic specialists, and more. They must always be on their toes. What can AI offer these overworked radiologists to improve their performance?

What advantages does AI have for radiology?

There are various ways AI might improve radiologists’ skills even further. We will go through a number of these methods in this section. This is not a complete list. There are several other ways that AI might help radiologists. Later on, we’ll add to this area.

Specify a more specific diagnosis

The goal of many AI systems is to provide more information. This might be accomplished by quantitating image-related details or through radiology image annotation, which are now only reported qualitatively. Alternately, the software can incorporate normative values, enabling doctors to contrast patient outcomes with a mean based on a general population sample.

The problem with this benefit is that we are frequently unsure how to manage this additional information. What does a particular value indicate? What does it signify for the diagnosis when a patient deviates significantly from the general population? There are frequently no rules (yet!) on what this information implies or what a radiologist should do because we have limited experience with quantified information.

Take on rote, everyday tasks.

AI is not proficient in all areas. Not yet, at least. What are the best tasks that AI can now handle? Lessons are easy to do, for which we have a tonne of data, and that doesn’t call for mixing many various types of input. Hence, radiologists perform a lot of straightforward everyday jobs. This usually refers to the most tedious activities or tasks that radiologists find difficult.

Reduce both intra- and inter-observer variation

Even radiologists with the highest levels of training and experience can have different diagnoses. After a long day at work, anything new could capture the eye of someone refreshed in the morning. Additionally, the emphasis on the findings of various radiologists may vary.

This is a challenge for referring doctors since they must consider these variances when putting all their knowledge together to make a final diagnosis. AI algorithms can reduce or even eliminate this variation in radiologist reports.

How might AI aid radiologists in treating patients?

The best patient results are the ultimate goal of any diagnostic procedure. Since medical imaging is a growing component of the diagnostic process, it should have the same result as other diagnostic procedures: the patient’s benefit.

Therefore, we should do the litmus test and determine whether any AI tool used by radiologists to evaluate pictures benefits the patient in the long run. Expressed, the quality and efficiency axes may be used to conceptualize patient benefits. Both are covered below.

Quality improvement for improved patient results

AI has enormous potential to improve the accuracy of the picture readings we now use. For instance, by carrying out analyses that are not being carried out because they would take too long for radiologists to carry out manually. An illustration would be volumetric measurements of organs, where hand delineation would be too time-consuming yet might increase the accuracy of the diagnosis.

AI also plays a significant role in the advancement of precision medicine. We can more precisely evaluate what information suggests that particular therapies will improve patient outcomes when more patient data becomes available. Patient communication is another process stage that might benefit from some AI input.

Increasing effectiveness for the patient’s benefit

Although the level of treatment quality is crucial, excellent care is useless if the diagnosis takes too long. As a result, efficiency and quality should always be blended. Several ways AI may assist enhance productivity. The automation of time-consuming manual processes can aid in accelerating the diagnosing process.

Choose the ideal tool for medical image annotation.

Without question, machine learning has the potential to revolutionize the healthcare sector.

Wide-ranging potential applications include the whole medical imaging life cycle, from picture production and analysis through diagnosis and prognosis.

These labels may occasionally be used in machine learning (ML). However, their format is frequently in sync with ML research requirements, such as lacking instance IDs, characteristics, a labeling queue, or the proper forms for deep learning frameworks like Pytorch or TensorFlow.

Collaborating with a reputed medical annotation company that has previously expended the time and energy necessary to adhere to the numerous data formats, regulatory regulations, and user experience required for a successful medical AI project is generally advisable.

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medical annotation

Five AI Applications That are Changing our World

Artificial intelligence is concerned with finding ways for computers to perform the same functions as humans. However, computers cannot make decisions on their own, i.e., they cannot make decisions without human intervention. The ability of computers to make decisions with their own intelligence makes machines operate and think like machines, which is what, in general terminology, is said to be as artificial intelligence (AI). There are myriad applications of AI in almost every field, which appear to enhance efficiency with minimal human intervention.

How AI can Reshape Our Future

Nowadays, AI is part of our everyday lives. Alexa, Google Home, and other smart speakers provide almost accurate results when we use them. The recommendation to complete sentences can be seen in email writing. Artificial intelligence is not only used in our day-to-day lives but also in military actions, warfighting, and weaponry. With the advancement of AI in cybersecurity, security systems are now capable of identifying cyber threats and creating a counter-attack security tool to deal with them strategically.

In recent trials, DeepMind from Google has beaten doctors in detecting breast cancer. AI is playing a substantial role in healthcare and pharmaceuticals. In pharmaceuticals, there is an increasing ability to combine different outcomes in a shorter amount of time and with greater accuracy. It is clear that AI is becoming increasingly prevalent in every aspect of our lives. With this tremendous advancement in artificial intelligence, it’s quite fascinating to imagine what our future will look like.

Our future will likely be dominated by applications of artificial intelligence that will affect many facets of our economy and society; many credible experts view this era as the most promising technological innovation and value creation in the foreseeable future. With improved data annotation services and the pace of innovations around AI & machine learning, AI is said to be holding the potential for reshaping our future in many ways.

The following are the major fields in which AI can be used, and we will discuss its future in each.

AI for Better Health Care

The use of artificial intelligence is capable of detecting disease based on symptoms without the need to visit a doctor. By analyzing the fitness band data and medical history, AI would suggest suitable medications and even deliver them to the customer’s fingertips through their phones.

Detecting fatal diseases like breast cancer has already been made easier by Google’s DeepMind. Detecting common diseases and suggesting proper medications are not far off when AI detects them. Thus, setting up medical appointments for prescriptions may not require human intervention.

AI in Education

AI-backed computer vision and machine learning systems are pushing growth across all industries, including education. Even though AI-based solutions have been available in the EdTech industry for quite some time, the industry has been slow to capitalize on them. Educators, however, were forced to rely on technology for virtual learning after the pandemic drastically changed the landscape.

All students have access to learning anytime and anywhere, thanks to AI-driven tools. AI can instantly answer students’ most frequently asked questions through support automation and conversational intelligence. As an added perk, students from around the globe can attend high-quality courses without incurring travel or living expenses

AI in Finance

Artificial intelligence allows businesses to create more space and improve security while ensuring they have a technologically advanced and innovative product. Financial services can be turbo-charged using artificial intelligence, and customers’ services can be transformed. As a result, products and services could be tailored to specific needs, internal processes could be more efficient, cybersecurity could be enhanced, and risk could be reduced.

In corporate finance, artificial intelligence is beneficial as it allows for better predicting and assessing loan risks. By improving loan underwriting and reducing financial risk, AI technologies, i.e., AI-powered computer vision and machine learning models, can make business processes more efficient with automation integration. In addition to bringing better data handling and improving customer experiences, AI has the potential to simplify, accelerate, and reimagine traditional processes.

AI in Military and Cybersecurity

Armed with artificial intelligence, military technologies are developing autonomous weapon systems that do not require human intervention, thereby enhancing security. Robots that are as intelligent as soldiers/commandos and capable of performing certain tasks may become available in the near future.

It is important to note that AI-assisted strategies will enhance mission effectiveness and provide the safest and most efficient way to execute missions. However, one of the concerns with AI-assisted systems is how the algorithms are performed.

AI Applications and the Scientific Method

Scientists have been concerned about the slowdown of scientific progress in recent decades. Discoveries may not be as abundant as they once were. AI now offers the possibility of innovating existing scientific discoveries and upgrading their mechanisms for greater efficiency and ease of use. As AI and machine learning (ML) develop, the capabilities of what we can accomplish will vastly increase.

Various ideas in human thought can be explored computationally. In addition to the ideas humans can handle with computers, there are many more that computers can do. The possibilities of human success with computers and artificial intelligence are much more comprehensive. The use of artificial intelligence enables unprecedented levels of analysis and pattern discovery of vast data sets. A new golden age of scientific revolution is coming as AI augments human intelligence, revolutionizing scientific research.


As we discussed in the article, artificial intelligence-assisted systems have the potential to improve the quality of our lives, we can conclude what our future path of technology is going to be laid on. Companies like Cogito, Anolytics, and others are emerging to empower AI and machine learning innovations through moderation, data annotation services, and quality training data delivery for machine learning models. Seeing how impactful it will be in the future, AI holds the potential to shape how the earthly life system on earth will function in the time ahead.

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Image Annotation : overview 2022 used various type of annotation

One of the most crucial phases in creating computer vision and image recognition systems, which entails identifying, getting, characterizing, and evaluating outcomes from digital images or videos, is image annotation.

AI applications frequently employ computer vision, including security, medical imaging, autonomous cars, and others. As a result, image annotation is essential for developing AI/ML in many fields and sectors.

Before we dig deep into the topic, let us discuss some basic nuances of image annotation.

Image annotation

Adding labels to an image is known as an image annotation. These labels are set by the AI engineer and are selected to provide details about what is seen in the image to the computer vision model.

react to section with heart react to section with light react to section with money react to section with thumbs-down The number of labels on an image might be different according to the project. A single label will sufficiently convey the complete image’s information (image classification) for specific projects. Other applications need many items labeled with distinct labels inside a single photograph.

The Process of Image Annotation. How does it work?

Three crucial elements are required to annotate an image:

1- Images

2 – Adding annotations to the photos

3 – A tool for adding annotations to the photos

Finding and training annotators to complete the annotation tasks is the first step in most image annotation initiatives. Although AI is highly specialized, annotating AI training data is not always necessary. While a master’s degree in machine learning is required to build a self-driving automobile, it is not needed to draw boxes around cars in photos (bounding box annotation). As a result, few annotators hold degrees in machine learning.

However, because each organization will have distinct needs, these annotators should get in-depth training on the requirements and norms of each annotation project.

After receiving training on annotating the data, the annotation team will begin working on hundreds or thousands of photos on a platform specifically designed for image annotation. This platform’s software needs to be equipped with all the tools required for the particular kind of annotation to be done.

What methods and techniques exist for image annotation?

There are many different image annotation methods and techniques, but it doesn’t imply you should use them all. Understanding the most popular image annotation methods and techniques along with use cases will help you decide which annotation tool to use for your project and what it requires.

Bounding boxes

Rectangles are drawn around symmetrical items like furniture, vehicles, and packages using bounding boxes. This facilitates object detection and algorithm localization, which is essential for driverless cars.

Self-driving automobiles can navigate the roadways securely with annotations of people, traffic signals, and vehicles. Bounding boxes come in two and three dimensions (cuboids).


Segmentation goes beyond object identification and picture categorization. With this technique, a picture is divided into several parts, each of which is given a label. Since each segment consists of pixels, each pixel has a label, which increases the accuracy of the annotation.

Three categories of segmentation exist:

1 Semantic segmentation

Semantic segmentation involves grouping an image into clusters and giving each group a label. Consider that we have a picture of three sheep. Each sheep will be counted as a separate cluster for semantic segmentation. The sky will serve as the background, which will also be considered.

2 Instance segmentation

Objects’ existence, position, form, and number are determined through instance segmentation. For example, instance segmentation may count the number of persons in a picture. Let’s revisit the sheep illustration. Even though they are given the same name, each sheep will be counted as a different instance in the instance segmentation scenario.

3 Panoptic segmentation

Instance segmentation and semantic segmentation converge in panoptic segmentation. It semantically segments the image and categorizes each pixel, identifying the instances to which each pixel belongs (instance segmentation). Each sheep will be tallied independently in our case, even if all the pixels in the image will be given names.

4 Skeletal Annotation

Skeletal annotation draws attention to body alignment and mobility. Annotators use this technique to connect lines on the human body by adding dots at the locations where the lines articulate. For instance, a line from a point on the wrist to a point on the elbow is then connected to the shoulder, and so on.

This produces a condensed representation of a body’s location that computers can recognize. This approach is used for video in most sports use cases, requiring annotators to precisely identify body postures over tens of thousands of individual frames.

5 Bitmask Annotation

This annotation style permits gaps or disjointed annotations by connecting individual pixels to particular objects.

6 Polygons

The edges of objects with asymmetrical shapes, such as rooftops, plants, and landmarks, are annotated using polygons.

7 Key-points

By placing dots around the target item, such as facial characteristics, body parts, and stances, key points are used to annotate microscopic shapes and details.

To achieve certain annotation objectives, any of the aforementioned annotation approaches can be combined. AI firms have freedom and alternatives when organizing annotation projects as the company can also do customised annotations as per the requirements.

Essential applications and benefits of Image annotation

When employing computer vision (CV) models, image annotation is extremely important for machine learning and artificial intelligence.

Across sectors, the advantages and significance of picture annotation services are becoming recognized. In 2023, the market for AI and machine learning data solutions is predicted to reach $1.2 billion.

Here are some examples of how businesses in various industries use image annotation services to their advantage.

Transportation: CV is used by self-driving automobiles, which are a reality today. Advanced machine learning algorithms must power these vehicles to ensure their efficiency and safety. Automobile makers may create intelligent apps for these autonomous vehicles with picture annotation.

Healthcare: In healthcare, CV can assist increase the precision of the diagnosis and raise the standard of care. With image annotation, CV systems may use CT scans, MRIs, and other diagnostic technologies to focus on patterns and problems like tumors or hairline fractures.

Agriculture: Precision agriculture combines established farming practices with cutting-edge technology to increase profitability, production, and sustainability. CV systems make it feasible to forecast agricultural output, assess plant health, and improve soil quality. In modern agriculture, robotics, GPS sensors, and drones play significant roles.

eCommerce and retail: Annotating images may improve clients’ user experiences and aid them in selecting the best items. When an annotation is appropriately done, each item on the website will have precise descriptions and labels. Annotation ensures that the products are correctly categorized, improving search results. Additionally, it helps to improve visual search. react to section with heart react to section with light react to section with money react to section with thumbs-down


The goal of image annotation is to make the most of technology, and it is something that will never go away. When key AI projects need image annotation, most AI organizations use expert data labeling services.

react to section with heart react to section with light react to section with money react to section with thumbs-down Businesses that provide expert data labeling services, like Cogito or Anolytics, appen, or any other, have skilled experts to help with data labeling and annotation. These companies can tailor their services to your unique needs as they provide seamless project management, quality control, and lower overheads. Originally published at – https://hackernoon.com/image-annotation-what-no-one-is-talking-about-in-2022