Text Sentiment Classification Method Based on Bilstm

Abstract

In the booming age of digital communication and natural language processing(NLP), understanding user sentiments expressed in texts has become increasingly vital. This research addresses the challenge of classifying text sentiments into three distinct categories: positive, neutral, and negative. Recognizing the nuances of human emotion in textual content is paramount for enhancing user experiences and tailoring personalized digital interactions. This study trains a BiLSTM model, supplemented with optimization strategies to adaptively learn from a diverse dataset. Additionally, a large number of baseline experiments were conducted to compare its efficacy with traditional algorithms such as Logistic Regression. The BiLSTM model demonstrated a good accuracy of 78.21% on the test set. Conclusively, while the proposed model shows significant potential, further refinements could be made, emphasizing data augmentation and model regularization to achieve optimal performance.