Three-Class Text Sentiment Analysis Based on LSTM

TL;DR

This paper presents a three-class sentiment analysis method for Weibo comments using LSTM networks, achieving high accuracy and outperforming traditional models, while discussing challenges like computational complexity and emotional nuance detection.

Contribution

The study introduces an LSTM-based approach for three-class sentiment classification on Weibo comments, demonstrating superior performance over existing methods.

Findings

Achieved 98.31% accuracy and 98.28% F1 score.

Outperformed conventional and other deep learning models.

Highlighted challenges in processing long texts and detecting sarcasm.

Abstract

Sentiment analysis is a crucial task in natural language processing (NLP) with applications in public opinion monitoring, market research, and beyond. This paper introduces a three-class sentiment classification method for Weibo comments using Long Short-Term Memory (LSTM) networks to discern positive, neutral, and negative sentiments. LSTM, as a deep learning model, excels at capturing long-distance dependencies in text data, providing significant advantages over traditional machine learning approaches. Through preprocessing and feature extraction from Weibo comment texts, our LSTM model achieves precise sentiment prediction. Experimental results demonstrate superior performance, achieving an accuracy of 98.31% and an F1 score of 98.28%, notably outperforming conventional models and other deep learning methods. This underscores the effectiveness of LSTM in capturing nuanced sentiment information within text, thereby enhancing classification accuracy. Despite its strengths, the LSTM model faces challenges such as high computational complexity and slower processing times for lengthy texts. Moreover, complex emotional expressions like sarcasm and humor pose additional difficulties. Future work could explore combining pre-trained models or advancing feature engineering techniques to further improve both accuracy and practicality. Overall, this study provides an effective solution for sentiment analysis on Weibo comments.