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

TL;DR

This paper applies the BERT model to Twitter sentiment analysis in SemEval2017, demonstrating improved accuracy over baseline models and discussing ethical considerations of using social media data.

Contribution

It introduces the use of BERT for Twitter sentiment analysis in SemEval2017, showing its effectiveness over traditional models in this context.

Findings

BERT outperforms Naive Bayes in accuracy, precision, recall, and F1 score.

BERT performs better on binary classification than multi-class tasks.

Ethical issues of using Twitter data are addressed.

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.