Sentiment Analysis on Multiple Indian Languages

Abstract

Sentiment analysis has become popular in the computer science community as it is essential for moderating and analyzing information across the internet. There are various applications for sentiment analysis, such as opinion mining, social media monitoring, and market research. Sentiment analysis in Indian languages is gaining importance due to the growth of content on social media, news articles, and other online platforms in Indian languages. Since India is a diversified country it has many languages that are used by millions of people but many Indian languages do not have enough resources for moderation on the internet to analyze the sentiment in the text to use them for either eradicating hate speech or to improve the productivity of companies by the understanding of customer needs from reviews. This paper explains an approach that helps in analyzing the sentiment which helps in content moderation and avoiding negativity on the internet. This approach uses the BERT algorithm for sentiment analysis in English. All the text in other languages will be translated into English and their sentiment is then analyzed. In this approach, we use the BERT algorithm for sentiment analysis on translated English text. This approach works well because sentiment analysis using BERT gives higher accuracy and the translation of text from Indian languages is made easy by the advent of natural language processing. By combining both the above-discussed processes we can analyze the sentiment in multiple Indian languages.