IIT Gandhinagar at SemEval-2020 Task 9: Code-Mixed Sentiment Classification Using Candidate Sentence Generation and Selection

TL;DR

This paper introduces a candidate sentence generation and selection method to improve sentiment classification of Hinglish code-mixed text using a Bi-LSTM neural network, addressing challenges posed by non-standard writing styles.

Contribution

It proposes a novel candidate sentence generation and selection approach that enhances sentiment analysis accuracy for code-mixed text over standard Bi-LSTM classifiers.

Findings

Improved sentiment classification accuracy on Hinglish data.

Demonstrated effectiveness of candidate sentence selection in code-mixed sentiment analysis.

Potential application to other code-mixing nuances like humor and intent detection.

Abstract

Code-mixing is the phenomenon of using multiple languages in the same utterance of a text or speech. It is a frequently used pattern of communication on various platforms such as social media sites, online gaming, product reviews, etc. Sentiment analysis of the monolingual text is a well-studied task. Code-mixing adds to the challenge of analyzing the sentiment of the text due to the non-standard writing style. We present a candidate sentence generation and selection based approach on top of the Bi-LSTM based neural classifier to classify the Hinglish code-mixed text into one of the three sentiment classes positive, negative, or neutral. The proposed approach shows an improvement in the system performance as compared to the Bi-LSTM based neural classifier. The results present an opportunity to understand various other nuances of code-mixing in the textual data, such as humor-detection, intent classification, etc.