IndiSentiBERT: Enhancing Multilingual Sentiment Analysis for Indian Languages Leveraging Noisy Parallel Corpus
Authors: Saurabh Kumar, Ranbir Singh Sanasam, Sukumar Nandi


Reviewer paper summaries: The paper is focused on sentiment analysis for low-resource Indic languages by fine-tuning IndicBERT v2 model using 2 publicly available datasets.
Summary of strengths: The paper is well-organized and the figure captions are informative.
Summary of weaknesses: The main weakness of the paper is the novelty is minute. Finetuning an existing model on existing corpora is not a big contribution. Additionally, considering the huge amount of work on sentiment analysis for Indic languages, the related work is very shallow and insufficient.
Comments, suggestions and typos: More extensive experiments, preferably those that could reveal novel findings could be done to improve the contributions in the submission. More work needs to be reviewed.
Overall assessment scores: 3
Metareview score: 2
Metareview: This paper proposes fine-tuning a Indic-specific LM on a sentiment dataset created by automatically translating English tweets to 22 Indian languages (IndiSentiment140). The authors find that their multilingual model outperform monolingual models on the test portion of IndiSentiment140, but performs poorly on a Indic-specific dataset (Societal).
Summary of reasons to publish: -  a new multilingual sentiment model for 22 Indian languages.