SemEval-2017 Task 4: Sentiment Analysis in Twitter using BERT
Rupak Kumar Das Dr. Ted Pedersen
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{\textsubscript{\tiny 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.