Sentiment analysis imparts indisputable analytical results from documents such as business reports, social media information, and many more. In Deep Learning, Sentiment analysis involves the process of identifying and classifying the subjective feature from the unstructured sample data. It automatically extracts features and provides intelligent decision-making on its own. The necessity of deep learning in sentiment analysis is accuracy with unstructured data and massive data handling capacity. It is mainly used in the text classification task and some of the techniques such as Sentiment classification - classifying all the documents based on the opinion of a certain sample, Feature-based sentiment classification - It takes the opinion of certain sample data, and Opinion summarization - It is the process of selecting the important data points by rewriting a few of the original sentences from the reviews in the classic text summarization. Convolutional Neural networks, Deep belief networks, Recurrent Neural Networks, and Long Recurrent Neural Networks (LSTMs) are the most commonly used deep learning algorithms for sentiment analysis.
Some of the specific applications of sentiment analysis that lead to exciting deep learning research in the future are sentiment composition, sarcasm analysis, emotion analysis, multi-modal data for sentiment analysis, and multilingual sentiment analysis. The popular applications areas of sentiment analysis are social media monitoring, marketing research, customer support, brand monitoring, and reputation management, and product analysis. Recent advancements in deep learning-based sentiment analysis are the Combined CNN-LSTM deep learning model for sentiment analysis, Spatiotemporal based sentiment analysis, to name a few.