Sentiment Analysis and Its Types

What is Sentiment Analysis?

Our human-powered text annotation empowers enterprises to identify and extract intent or sentiment. Under Sentiment analysis we inspect user input and identify the prevailing subjective opinion, especially to determine a user’s attitude as positive, negative, or neutral. When making a detect intent request, you can specify that sentiment analysis is performed, and the response will contain sentiment analysis values.

Use of Sentiment Analysis 

Sentiment Analysis is a trendy scientific and market research in the field of Natural Language Processing and Machine Learning. NLP Services has become a very important tool for many new business functions, from chatbots, intelligent search, and question answering systems, and especially sentiment analysis. Sentiment analysis tool categorizes pieces of writing as positive, neutral, or negative through Accurate Data Labeling and annotation services and proper Sentiment Analysis Tools. There are many types of tools that are used in sentiment analysis. 

Sentiment analysis uses NLP and interprets and classifies emotions. Sentiment analysis is often used in business to detect sentiment and understand customers and their feedback. 

Types of Sentiment Analysis 

1. Intent Analysis 

Intent analysis helps in understanding the intention behind a message and identifies some basic information to analyze whether it’s an opinion, news, advertisement, complaint, suggestion, appreciation, or query, positive-negative, neutral, etc.

Each and every business needs genuine feedback from the customers to make their products and services more valuable and improve accordingly. If feedback given by customers is not understood by businesses there is no use of feedback Thus Intent analysis will play a major role here. It will help your business to understand what your customer is trying to say by giving you feedback.

Thus, Understand the intent of customers. 

2. Emotion Detection 

This type of sentiment analysis aims at detecting emotions e.g. Happy, sad, angry. Many emotion detection systems use complex machine learning algorithms.

Basically, Lexicons & machine learning are used to understand the sentiment. Lexicons are lists of words that are either positive or negative. This makes it easier to separate the terms according to their sentiment.

3. Fine-grained Sentiment Analysis

Fine-grained Sentiment Analysis includes determining the polarity of the opinion. It can be a simple binary positive/negative sentiment differentiation. This type can also go into the higher specification (for example, very positive, positive, neutral, negative, very negative) 

This is usually referred to as fine-grained sentiment analysis and is used to interpret 5-star ratings in the review section like 5 stars means very positive While 1 star means very Negative

Through various Data annotations including image annotation & deep learning techniques, AI became more smart and intelligent, but a different problem emerged: lack of data and through a large number of Data labeling provided by data labeling companies it taught AI how to understand what customers are saying, you can’t exactly have it talk to itself to generate new training data. As an outcome, it was clear that in order to build an AI that could learn how to understand customer feedback and add value to the conversation and provide more sources of knowledge to AI and that made sentiment analysis simple. 

It’s very necessary to have annotated data in sentiment analysis and it helps to improve customer experience in many industries 

Conclusion

Sentiment analysis is very necessary for many industries in many ways and AI is helping it grow in many simpler ways 

Learning spiral, Data Labeling company provides data annotation services and helps you to create and enhance machine learning models with utmost accuracy.  Pick the best data labeling and Data annotation company for computer vision, NLP projects while saving money and time!