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Sentiment analysis has been a topic of discussion in the exploration domain of language understanding. Yet, the neural networks deployed in it are deficient to some extent. Currently, the majority of the studies proceeds on identifying the sentiments by focusing on vocabulary and syntax. Moreover, the task is recognised in Natural Language Processing (NLP) and, for calculating the noteworthy and exceptional results, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) have been employed. In this study, we propose a four-phase framework for Twitter Sentiment Analysis. This setup is based on the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model as an encoder for generating sentence depictions. For more effective utilisation of this model, we deploy various classification models. Additionally, we concatenate pre-trained representations of word …