Design and Implementation of Aspect-Based Sentiment Analysis Task

Abstract

This paper presents the design and implementation of a deep-learning-based aspect-level sentiment analysis model for the aspect term extraction and sentiment polarity classification of Chinese text comments. The model utilizes two layers of BERT models and fully connected neural networks for feature extraction and classification. It also incorporates a context feature dynamic weighting strategy to focus on aspect words and enhance the model’s performance. Experimental results demonstrate that the proposed model performs well in aspect-level sentiment analysis tasks and effectively extracts sentiment information from the text. Additionally, to facilitate user interaction, a lightweight system is built, which enables model invocation and the visualization of analysis results, offering practical value.

Keywords

• natural language processing
• aspect-level sentiment analysis
• BERT model
• deep learning