Sentiment Analysis using BERT, CNN and BI-LSTM

Abstract: Sentiment Analysis (SA) has emerged as a critical task in Natural Language Processing (NLP), especially with the exponential growth of user-generated content. Although deep learning models like Bi-directional Long Short-TermMemory(BiLSTM),Convolutional Neural Networks (CNN), and Bidirectional Encoder Representations from Transformers (BERT) have greatly improved sentiment classification accuracy, they often fail to capture the meaning of idiomatic expressions. In this paper, we propose a hybrid sentiment analysis framework that integrates BERT embeddings with CNN for feature extraction, Bi-LSTM for sequence modeling, and an attention mechanism for enhanced sentiment prediction. Additionally, we introduce a dynamic idiom detection and sentiment inference module that identifies both known and unknown idioms. For unknown idioms, contextual information is retrieved via web search to lexically infer sentiment. The model is evaluated on a benchmark English hotel review dataset, along with an extended idiom-labeled dataset. Our approach achieves a state-of-the-art accuracy of 92.24%, outperforming traditional Bi-LSTM and RNN models. Results show that our hybrid system significantly enhances sentiment classification performance, particularly in the presence of idiomatic expressions