Companies Using Sentiment Analysis Work Done in Multiple Languages

 Cogito World Class Environment for Sentiment Analysis 

Famous company on data annotation for machine learning process. 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.

Cogito - Work Done in Multiple Languages

Problem definition for 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.

Uses of sentiment analysis in social media

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.

Sentiment analysis using machine learning 

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. Afterwards, 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 a 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 analyse grammatical nuances.

Rule-based sentiment analysis

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 the psychographic segmentation in sentiment analysis can give more comprehensive perception of different kinds of people interacting with each other.

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