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.

Summing-up

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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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.

TYPES OF SENTIMENT ANALYSIS

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.

conclusion

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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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

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

Conclusion

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

Remove Unwanted Contents from Internet

Cogito Social media content moderation offered by Cogito to moderate all types of unwanted contents like objectionable images, or offensive videos containing nudity, sexuality and violence or other disturbing contents against the community of social media platforms and networking sites.

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What is Data Annotation?

Making the data understandable to machines by labeling using the certain techniques like outlining or shading the text or objects images is basically known as data annotation. The data can be anything from simple text to images or videos and audios available in various formats.

The main motive of data annotation is highlighting the important words, texts or objects using the annotation techniques to make it recognizable to machines or computer vision used for machine learning or artificial intelligence model developments.

Types of Data Annotation

There are different types of data annotation services offered by companies providing machine learning and AI training data. Text Annotation, Image Annotation, Audio Annotation, Video Annotation are the leading types of annotation services you can find in the market. And under each types there are certain annotation techniques like image annotation bounding boxes, cuboid, semantic, polygons and point or polylines are the popular annotation methods.

Types of Data Annotation

Data Annotation for AI and Machine Learning

Apart from few human oriented needs, most of the annotation is done for machine learning and AI data training. Data annotation techniques helps the machines to recognize the actual dimension, shape, size and types of content available on the web based services. Each data has its own format and most of them are understandable to humans but to make such data comprehensible to machines a precise data annotation services is required.

How to Get Annotated Data for Machine Learning or AI?

Developing the machine learning or AI models not only required special skills or knowledge but having a high-quality training data is also important to make such models functional and give the accurate results. Getting the quality training data with right annotation and labeling is difficult, especially if you are looking for free data annotation service.

Cogito is one the well-known companies offers the high-quality machine learning training data with proper annotation and data labeling. It is expert in image annotation and data labeling service for various industries like healthcare, automobiles, retail, agriculture, ecommerce, banking finance and information technology fields with well-diversified clients portfolio.

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.

What is Best Data Labeling Process to Create Training Data for AI?

Data annotation in AI world is one of the most crucial processes to make available the set of training data for machine learning algorithms. And computer vision based AI model needs annotated images to make the various objects recognizable for better understanding of surroundings.

Data annotation process involves from collection of data to labeling, quality check and validation that makes the raw data usable for machine learning training. For supervised machine learning projects, without labeled data, it is not possible to train the AI model.

During the whole process, well trained human power with right tools and techniques, data is annotated as per the requirements and then processed in a highly secured environment to clients. The data is encrypted to make sure it can be safely delver to the clients to avoid any risk. So, right here we will discuss about the data labeling process to step wise facts.

DATA LABELING PROCESS

Collection of Datasets

The first step towards data annotation is understand the problem to provide the precise training data. Hence, collecting the datasets from client is an important aspect. So, the raw data is collected directly from the client in the well-organized format.

Also Read : How to Create Training Data for Machine Learning?

The data is collected through a proper channel to make sure its originality and security. Many business enterprises follow the different routes to send the data for labeling. Sometimes it is supplied in encrypted format and after data annotation it is again sent to client in the secured format.

Labeling of Dataset

After acquiring the data, organizing the labeling process is the next part of data labeling. Actually, for the supervised machine learning labeled data is required, and proper labeling is important to make sure AI model get trained precisely and work in the right manner.

Choosing the right tools and technique is another factor for data labeling. And in image annotation is done to create the training data sets for computer vision based AI model. The quality is also need to be ensured to make sure the model can predict with the accurate results. To consider all these points two points also need to discussed here – how to label data and who will label the data.

How to Label Data: After getting the data set for labeling, the annotation team has to decide the type of annotation applied here, like detecting, classifying and segmentation of the object. Here if client provides the specific tool or software, then annotators use to annotate the images using the same.

Once the data sets are assigned to annotators and instructed what type of annotation and what are the tools will be best suitable to annotate the data.

Who Will Label the Data: Similarly, the next step into data labeling process comes, who will annotate or label the data. Here, two options are available for the AI companies – first organize the in-house data labeling facility which could be easy control for you and might cost less but it can take extraordinary time due to collection and labeling of entire data sets.

The second option is outsource the labeling task to other data annotation companies, who have team of well-trained and experienced annotators to label the data for machine learning with better efficiency and quality. The best part of outsourcing is data has the ability to aggregate quickly. While on the other hand transparency, accuracy and high-cost are the concerning factors with outsourcing services.

Quality Check and Evaluation

After annotating the data, checking the quality is one of the most important factors of data labeling process. Here, qualified annotator manually check the quality of each annotated images to make sure machine learning algorithm get trained with right accuracy.

Also Read : How to Build Training Data for Computer Vision?

Here, the data sets are also evaluated to validate the same, and if there is any correction the data is annotated correctly and finally validated for machine learning training. Here highly experienced, annotators are required to prudently the check the quality of data labeled to make sure AI companies get the best and high-quality datasets at best pricing.

Final Delivery of Annotated Datasets

The last step in data annotation process is after labeling, the data need to be safety delivered to client. Here again the authenticity and privacy of data is ensured till the data is delivered to client. And the mode of delivering the data also depends on the company to company but there should be safe mode to send such data with complete confidentiality and safety.

Data Labeling Process at Cogito

Most of the companies follow the above discussed data labeling process but few companies have more complex or even more sophisticated but secured data annotation process. Cogito is one the companies providing the world-class data labeling solution with next level of accuracy. It is following the international standards for data security and privacy to ensure the originality of AI model.

How to Data Sets Annotated for Sentiment Analysis in the News Headlines?

To make understandable the sentiments of the people by the AI model, a huge amount of training data sets required for machine learning. And language annotation is the right process to annotate the key texts in a document and make it comprehensible to machines.

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