  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.
