JUNLP@Dravidian-CodeMix-FIRE2020: Sentiment Classification of Code-Mixed Tweets using Bi-Directional RNN and Language Tags

TL;DR

This paper presents a novel approach using bi-directional LSTMs and language tags to improve sentiment classification of code-mixed Tamil tweets from social media, addressing challenges posed by multilingual and informal text.

Contribution

It introduces a combined model of bi-directional RNNs and language tagging specifically for sentiment analysis of code-mixed Tamil social media texts, which is a novel application.

Findings

Achieved F1 score of 0.58 on test data.

Demonstrated effectiveness of language tags in code-mixed sentiment analysis.

Improved sentiment classification accuracy for Tamil-English social media texts.

Abstract

Sentiment analysis has been an active area of research in the past two decades and recently, with the advent of social media, there has been an increasing demand for sentiment analysis on social media texts. Since the social media texts are not in one language and are largely code-mixed in nature, the traditional sentiment classification models fail to produce acceptable results. This paper tries to solve this very research problem and uses bi-directional LSTMs along with language tagging, to facilitate sentiment tagging of code-mixed Tamil texts that have been extracted from social media. The presented algorithm, when evaluated on the test data, garnered precision, recall, and F1 scores of 0.59, 0.66, and 0.58 respectively.