M2SA: Multimodal and Multilingual Model for Sentiment Analysis of Tweets

Gaurish Thakkar, Sherzod Hakimov, Marko Tadić
4/2/2024
cs.CL

No official code found. Best implementation: cleopatra-itn/M2SA-multimodal-multilingual-sentiment-analysis (7 stars, last commit 2024-06-05).

Abstract

In recent years, multimodal natural language processing, aimed at learning from diverse data types, has garnered significant attention. However, there needs to be more clarity when it comes to analysing multimodal tasks in multi-lingual contexts. While prior studies on sentiment analysis of tweets have predominantly focused on the English language, this paper addresses this gap by transforming an existing textual Twitter sentiment dataset into a multimodal format through a straightforward curation process. Our work opens up new avenues for sentiment-related research within the research community. Additionally, we conduct baseline experiments utilising this augmented dataset and report the findings. Notably, our evaluations reveal that when comparing unimodal and multimodal configurations, using a sentiment-tuned large language model as a text encoder performs exceptionally well.

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Code Implementations(1)

cleopatra-itn/M2SA-multimodal-multilingual-sentiment-analysisCommunity100%

Official repository for the paper "M2SA: Multimodal and Multilingual Model for Sentiment Analysis of Tweets"

70Shell, PythonMay 24, 20232 years ago

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Cite this paper

@article{thakkar2024msa,
  title  = {M2SA: Multimodal and Multilingual Model for Sentiment Analysis of Tweets},
  author = {Gaurish Thakkar and Sherzod Hakimov and Marko Tadić},
  year   = {2024},
  eprint = {2404.01753},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.01753v3}
}

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