NITS-Hinglish-SentiMix at SemEval-2020 Task 9: Sentiment Analysis For Code-Mixed Social Media Text Using an Ensemble Model

TL;DR

This paper presents NITS-Hinglish-SentiMix, an ensemble model designed for sentiment analysis of Hinglish code-mixed social media text, achieving an F-Score of 0.617 in SemEval-2020 Task 9.

Contribution

It introduces a novel ensemble approach specifically tailored for sentiment analysis of Hinglish code-mixed social media data.

Findings

Achieved an F-Score of 0.617 on test data

Demonstrated effectiveness of ensemble modeling for code-mixed sentiment analysis

Addressed the challenge of analyzing mixed-language social media text

Abstract

Sentiment Analysis is the process of deciphering what a sentence emotes and classifying them as either positive, negative, or neutral. In recent times, India has seen a huge influx in the number of active social media users and this has led to a plethora of unstructured text data. Since the Indian population is generally fluent in both Hindi and English, they end up generating code-mixed Hinglish social media text i.e. the expressions of Hindi language, written in the Roman script alongside other English words. The ability to adequately comprehend the notions in these texts is truly necessary. Our team, rns2020 participated in Task 9 at SemEval2020 intending to design a system to carry out the sentiment analysis of code-mixed social media text. This work proposes a system named NITS-Hinglish-SentiMix to viably complete the sentiment analysis of such code-mixed Hinglish text. The proposed framework has recorded an F-Score of 0.617 on the test data.