SemEval-2017 Task 4: Sentiment Analysis in Twitter using BERT

Rupak Kumar Das, Ted Pedersen
1/15/2024
cs.CL

No official code found. Best implementation: rupakdas18/SemEval-2017-Task-4-A-B-C-using-BERT (4 stars, last commit 2022-04-12).

Abstract

This paper uses the BERT model, which is a transformer-based architecture, to solve task 4A, English Language, Sentiment Analysis in Twitter of SemEval2017. BERT is a very powerful large language model for classification tasks when the amount of training data is small. For this experiment, we have used the BERT(BASE) model, which has 12 hidden layers. This model provides better accuracy, precision, recall, and f1 score than the Naive Bayes baseline model. It performs better in binary classification subtasks than the multi-class classification subtasks. We also considered all kinds of ethical issues during this experiment, as Twitter data contains personal and sensible information. The dataset and code used in our experiment can be found in this GitHub repository.

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Code Implementations(1)

rupakdas18/SemEval-2017-Task-4-A-B-C-using-BERTCommunity100%
41Shell, PythonMay 7, 20214 years ago

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Cite this paper

@article{das2024semeval,
  title  = {SemEval-2017 Task 4: Sentiment Analysis in Twitter using BERT},
  author = {Rupak Kumar Das and Ted Pedersen},
  year   = {2024},
  eprint = {2401.07944},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2401.07944v2}
}

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