Text-Based Sentiment Analysis Using Deep Learning Techniques

Today’s world is the word of the internet and word of information. In this modern area of development, technology has contributed a lot to the global platform called Web. Reviews and emotions play a vital role in our day to day lives as they help in learning communication, decision making, product evaluation, election prediction. Artificial Intelligence (AI) is the branch of computer science that has worked on the analysis of the reviews as well as opinions generated by the people, and helps the media in order to cope with the situation. Currently to improve the marketing strategy and product advertisement traditional web-based survey methods have been replaced with the Sentiment Analysis which improves customer service. Therefore, various approaches such as machine learning, lexicon-based, hybrid, and other approaches were used to analyze these sentiments/opinions in the past. With the current advancements in deep neural networks, deep learning-based methods are becoming very popular due to their accuracy enhancement in recent times. Various methods like Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bi-LSTM (Bidirectional LSTM) are used for sentiment analysis. This work highlights different deep learning techniques used for text -based sentiment analysis for reviews generated by users.