Methods of Sentiment Analysis for Hindi and English Languages

Abstract

Social media is widely regarded as one of the most important unstructured data. Analyzing and extracting meaning from such data is a time-consuming process. Because of the enormous data available on social media platforms, sentiment extraction has gotten a lot of attention. Microblogging is a relatively new phenomenon, with Twitter being the most widely utilized. It's one of the most comprehensive free and open data sources available. Today's society sees a lot of differing viewpoints on Twitter. Researchers can use opinion mining to obtain the present emotion and mood of the public. Sentiment Analysis is defined as the technique of extracting and finding the polarity of a given material to get insight into the hidden information, emotion, feeling contained within a text. The ultimate objective of sentiment analysis is to extract meaningful material from various sources of information. The first analysis of tweets was done using the Natural Language Processing (NLP) method. For further analysis of the opinionated data, two approaches are available: the Lexicon Based Approach (LBA) and the Machine Learning Approach (MLA) based on supervised learning. The LBA approach employs a resource dictionary, namely the Hindi SentiWordNet, and a Hybrid Based Approach (HBA) that joins the Lexicon based and Machine learning for categorizing tweets as positive or negative