Multi-lingual Twitter sentiment analysis using machine learning

Abstract

Twitter Sentiment Analysis is one of the leading research fields nowadays. Most of the researchers have contributed to the research in twitter sentiment analysis in English tweets, but few researchers have focused on the multilingual twitter sentiment analysis. Still, some more challenges are present and not yet addressed in the domain of multilingual twitter sentiment analysis (MLTSA). Research is highly warranted in these unexplored areas. This study presents the implementation of sentiment analysis in multilingual twitter data and improves the data classification up to the adequate level of accuracy. Twitter is the sixth leading social networking site in the world. Active users for twitter in a month are 330 million. People can tweet or retweet in their languages and allow users to use emoji's, abbreviations, contraction words, misspellings, and shortcut words. The best platform for sentiment analysis is twitter. Multilingual tweets and data sparsity are the two main challenges. In this paper, the MLTSA algorithm gives the solution for these two challenges. MLTSA algorithm is divided into two parts. One for detecting and translating non-English tweets into English using natural language processing (NLP) and the second one is an appropriate pre-processing method with NLP support that can reduce the data sparsity. The result of the MLTSA with SVM achieves good accuracy by up to 95%.