Sequence-Aware Sentiment Analysis Using Bi-LSTM and Conditional Random Field for Contextual Understanding

Sentiment analysis is a fundamental aspect of natural language processing (NLP) that facilitates the extraction and interpretation of emotions and opinions embedded in textual data. Traditional sentiment analysis models often fail to capture consecutive dependencies and contextual information within sentences, leading to inaccurate sentiment predictions. This study introduces a sequence-aware sentiment analysis framework that integrates Bidirectional Long Short-Term Memory (Bi-LSTM) with a Conditional Random Field (CRF) layer. While Bi-LSTM effectively captures long-range dependencies in textual data, the CRF component ensures consistency in label assignments by refining sequential predictions. The proposed model is evaluated against standard sentiment classification standards, demonstrating improved performance compared to conventional deep learning approaches. The study stated the significance of sequential modeling in sentiment analysis, showcasing the benefits of combining Bi-LSTM and CRF in understanding contextual dependencies. The attention mechanisms and contextual influence maps further express that the CRF layer significantly improves the model's ability to weigh important words in a sequence, leading to more accurate sentiment predictions. Experimental results indicate an improvement in recall, F1 score, accuracy, and validating the model's effectiveness in sentiment prediction.