Bi-LSTM and Ensemble based Bilingual Sentiment Analysis for a Code-mixed Hindi-English Social Media Text
Konark Yadav, Aashish Lamba, Dhruv Gupta, Ansh Gupta, Purnendu Karmakar, Sandeep Saini
Abstract
India is a multilingual and multi-script country and a large part of its population speaks more than one language. It has been noted that such multilingual speakers switch between languages while communicating informally. The code-mixed language is very common in informal communication and social media, and extracting sentiments from these code-mixed sentences is a challenging task. In this work, we have worked on sentiment classification for one of the most common code-mixed language pairs in India i.e. Hindi-English. The conventional sentiment analysis techniques designed for a single language don't provide satisfactory results for such texts. We have proposed two approaches for better sentiment classification. We have proposed an Ensembling based approach which is based on hybridization of Naive Bayes, SVM, Linear Regression, and SGD classifiers. We have also developed a bidirectional LSTM based novel approach. The approaches provide quite satisfactory results for the code-mixed Hindi-English text.