An Effective Framework for Sentiment Analysis of Hindi Sentiments Using Deep Learning Technique

Sentiment analysis is a way to extract emotion-based information or users' sentiments and opinions from text data. Sentiment analysis uses text analysis, Natural Language Processing (NLP), and computational linguistics. Text mining research has a strong focus on Sentiment Analysis (SA), which deals with the processing of opinions, attitudes, and the subjective aspect of the text. In today’s time, we are seeing the availability of extensive web-based data on the Hindi language, which is a national language of India and also a first language used by the majority of the population in India. So, it has become extremely important to analyze customer's/user's opinions about the product, services or company and find out the key insights, particularly for companies and government organizations. The insights of sentiment analysis give a ray to the organizations to set their product/services or company as a key player in the market to survive to the long time period and get good business. This paper explores how we can effectively use deep neural networks in sentiment analysis to classify Hindi sentiments. To do it, we used word embeddings for Hindi text data and then trained the proposed model using Long Short-Term Memory (LSTM) and, subsequently, employed parameters.