Hybrid Framework for Sentiment Analysis Using ConvBiLSTM and BERT

Abstract

Sentiment analysis is specifically a text mining technique that utilizes natural language processing to computerize the process of analyzing text that aims to determine the sentiment expressed. The fundamental purpose of sentiment analysis is to get valuable insights that lead to all-around development in specific domains. The fantastic applications of sentimental analysis include monitoring social media, management of customer support, and customer reviews research. One of the major pitfalls in sentiment analysis is word ambiguity. To overcome this drawback, a proposed hybrid framework presented in this work is capable of dealing with such ambiguity issues. The considered evaluation parameters are accuracy, F1 score and time taken. The proposed hybrid framework utilizes Convolutional Bi-directional Long short-term memory network (ConvBiLSTM) with Bidirectional Encoder representations from Transformer (BERT) tokeniser on the given dataset and outperform other methodologies with 95.10% accuracy.