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This paper introduces a study on tweet sentiment classification. Our task isto classify a tweet as either positive or negative. We approach the problem intwo steps, namely embedding and classifying. Our baseline methods includeseveral combinations of traditional embedding methods and classificationalgorithms. Furthermore, we explore the current state-of-the-art tweet analysismodel, BERTweet, and propose a novel approach in which features are engineeredfrom the hidden states and attention matrices of the model, inspired byempirical study of the tweets. Using a multi-layer perceptron trained with ahigh dropout rate for classification, our proposed approach achieves avalidation accuracy of 0.9111.

Classifying Tweet Sentiment Using the Hidden State and Attention Matrix of a Fine-tuned BERTweet Model