RSM-NLP at BLP-2023 Task 2: Bangla Sentiment Analysis using Weighted and Majority Voted Fine-Tuned Transformers

TL;DR

This paper presents a Bangla sentiment analysis system using fine-tuned transformer models with ensemble techniques, achieving competitive results in a shared task and addressing the underexplored area of Bangla NLP.

Contribution

The paper introduces an ensemble approach with weighted and majority voting on fine-tuned BERT models specifically for Bangla sentiment analysis, a relatively under-researched language.

Findings

Achieved 0.711 accuracy in multiclass sentiment classification

Outperformed individual models with ensemble methods

Secured 10th place in the shared task leaderboard

Abstract

This paper describes our approach to submissions made at Shared Task 2 at BLP Workshop - Sentiment Analysis of Bangla Social Media Posts. Sentiment Analysis is an action research area in the digital age. With the rapid and constant growth of online social media sites and services and the increasing amount of textual data, the application of automatic Sentiment Analysis is on the rise. However, most of the research in this domain is based on the English language. Despite being the world's sixth most widely spoken language, little work has been done in Bangla. This task aims to promote work on Bangla Sentiment Analysis while identifying the polarity of social media content by determining whether the sentiment expressed in the text is Positive, Negative, or Neutral. Our approach consists of experimenting and finetuning various multilingual and pre-trained BERT-based models on our downstream tasks and using a Majority Voting and Weighted ensemble model that outperforms individual baseline model scores. Our system scored 0.711 for the multiclass classification task and scored 10th place among the participants on the leaderboard for the shared task. Our code is available at https://github.com/ptnv-s/RSM-NLP-BLP-Task2 .