RSM-NLP at BLP-2023 Task 2: Bangla Sentiment Analysis using Weighted and
  Majority Voted Fine-Tuned Transformers
Pratinav Seth Rashi Goel Komal Mathur Swetha Vemulapalli
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 .