Twitter sentiment analysis using deep learning models
Arnab Roy, Muneendra Ojha
Abstract
Twitter is a massive repository and a gold mine of human thoughts that expresses a person's instant feeling. A retrospective review of tweets during the ensuing COVID-19 pandemic may provide valuable insights into the understanding of a person's feelings. With an enormous amount of data, training a model to understand the precise feeling is a daunting job. There are many developments recorded in the field of deep learning to pave the way forward. The purpose of this paper is to compare the tweet sentiment classification using Google BERT, attention based Bidirectional LSTM and Convolutional Neural Networks (CNNs). The final models are trained on the Twitter dataset SemEval-2016, where the embeddings are fine-tuned again. Such models proved to be highly effective and accurate in the study of emotions as opposed to machine learning techniques.