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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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

Big data analytics and AI importance in media and entertainment industry

The media and entertainment industry is also utilizing the power of Artificial Intelligence (AI) in making the visual content more interactive and interesting. It is helping to serve the audience a data-intensive and personalize automated content making their viewing experience more interesting and entertaining.

While on the other hand, the entertainment industry needs such innovative technology to make the audience experience more enjoyable and user-friendly. AI companies are also consistently working to integrate this technology into various sub-fields of the entertainment sector improving its efficiency.

AI Applications in Media and Entertainment

The application of AI in media and entertainment is witnessing rapid growth especially in terms of distributing and showing visual content. Media companies face challenges while developing the content in high quantity while ensuring the quality, hence they adopt AI to achieve this objective.

And once they achieve their objective, they improve their services enhancing the experience of the customers. So, we have discussed here the use of AI in the media and entertainment industry with use cases and examples.

Personalizing the Content & Recommendations

From music app to OTT platforms, the audio, as well as visual contents, can be personalized as per the preferences and previous experiences. Using machine learning, users behavior and demographic details, music or videos are recommended like what kind of movies they like most.

AI using advanced machine learning algorithms and deep learning networks helping in delivering personalized content to users. And this kind of highly personalized experience for users helps media or entertainment companies increase their user base and offer a better service compared to their competitors.

Online Advertising with Targeted Audience

Online advertisements in the media industry are playing a significant role in branding and business promotions. And AI is used to make online advertising more precise and productive with a targeted audience for higher conversion rates.

The best example is Google Adsense and Adwords that can use the user’s history like what kind of products they were searching for or browsing on web or eCommerce sites. And this kind of AI-based sensing helps AI to show the ads as per the user’s preferences. It helps advertisers to target the right audience and get maximum outputs from the Ads.

Controlling the Online Content Broadcasting

The online media and entertainment industry is running with the help of different types of content. And apart from general topics or common subjects, there is objectionable content broadcasted through TV, Online Music Channels or OTT platforms.

Controlling such content is mandatory from the regulatory authorities. Again AI helps here in detecting and filtering such objectionable contents. AI can identify the user’s age and gender before showing such content or using the automated content moderation service to moderate the objectionable content before broadcasting with the audience category ratings like suitable for children or only for adults.

Classification & Categorization of Content

Online streaming platforms like YouTube or OTT have different genre of music videos, songs, movies or TV shows. And these online platforms are using AI algorithms to show the content as per the user’s preference and choice.

The AI-enabled system can detect the objects, data, visual scenes and other details about the movies like genre, cast and crew members to show them similar content. Implementing the AI in media and entertainment industry can automate the categorization and classification of content for a better users experience.

Meta Tagging Subtitles & Automated Transcription

Content published in the media and entertainment industry needs to make comprehensible to the audience. Hence, AI can help in identifying the videos and other online content to classify them with meta tags and descriptions.

Apart from that movies, music videos and TV shows are transcribed into different languages using AI-based technologies like natural language processing through machine learning and deep learning. The voice of movies is converted into different languages with subtitle and audio annotation to make it understandable to a wider populace for more engagements of the users. Powered by Cogito Blog

Which is better for image classification, supervised or unsupervised classification?

Image classification is actually fundamental task that helps to classify and comprehend the entire image as a whole. The main motive of image classification is to classify the image by assigning it to a specific label.

Usually, Image Classification to images in which only one object appears and that is only analyzed. In contrast, object detection involves both classification and localization tasks, and is used to analyze more realistic cases in which multiple objects may exist in an image.

Supervised vs Unsupervised Classification

Supervised classification is based on the idea that a user can select sample pixels in an image that are representative of specific classes and then direct the image processing software to use these training sites as references for the classification of all other pixels in the image.

The classification is the process done with multi-step workflow, while, the Image Classification toolbar has been developed to provide an integrated environment to perform classifications with the tools.

Not only does the toolbar help with the workflow for performing unsupervised and supervised classification, it also contains additional functionality for analyzing input data, creating training samples and signature files, and determining the quality of the training samples and signature files.

Supervised Image Classification

Supervised classification uses the spectral signatures obtained from training samples to classify an image. With the assistance of the Image Data Classification toolbar, you can easily create training samples to represent the classes you want to extract. You can also easily create a signature file from the training samples, which is then used by the multivariate classification tools to classify the image.

In supervised classification, you select representative samples for each land cover class. The software then uses these “training sites” and applies them to the entire image.

For supervised image classification, you first create training samples. For example, you mark urban areas by marking them in the image. Then, you would continue adding training sites representative in the entire image.

Unsupervised Image Classification

Unsupervised classification finds spectral classes (or clusters) in a multiband image without the analyst’s intervention. The Image Classification toolbar aids in unsupervised classification by providing access to the tools to create the clusters, capability to analyze the quality of the clusters, and access to classification tools

In unsupervised classification, it first groups pixels into “clusters” based on their properties. Then, you classify each cluster with a land cover class.

Overall, unsupervised classification is the most basic technique. Because you don’t need samples for unsupervised classification, it’s an easy way to segment and understand an image.

Which one is better?

No doubt, unsupervised classification is fairly quick and easy to run. There is no extensive prior knowledge of area required, but you must be able to identify and label classes after the classification. The classes are created purely based on spectral information, therefore they are not as subjective as manual visual interpretation.

While on the other hand, one of the disadvantages of unsupervised classification is that the spectral classes do not always correspond to informational classes. The user also has to spend time interpreting and label the classes following the classification. Spectral properties of classes can also change over time, so you can’t always use the same class information when moving from one image to another.

Both have their own advantages and disadvantages, but for machine learning projects, supervised image classification is better to make the objects recognized with the better accuracy. Overall, object-based classification outperformed both unsupervised and supervised pixel-based classification methods. And depending on the AI model or machine learning algorithms compatibility, image classification process is followed to classify the images with better accuracy and quality object detection.

How to Find Best Image Annotation Company in India?

Image annotation is becoming the need of AI industry to train the machines with large volume of visual data sets. It is a kind of object lebelling in images making recognizable for computer vision that helps machine learning algorithms to understand the similar objects. And there are various types of image annotation techniques applied while doing this job.

Continue reading “How to Find Best Image Annotation Company in India?”