Revolutionizing Chinese sentiment analysis: A knowledge-driven approach with multi-granularity semantic features
Ping He, Jiwei Tian, Jiwei Tian, Jiwei Tian, Jiwei Tian
TL;DR
This paper introduces a new method for Chinese sentiment analysis that combines domain knowledge and multi-granularity features to improve accuracy.
Contribution
A novel knowledge-driven approach integrating semantic features and emotional knowledge for Chinese sentiment analysis.
Findings
The method achieved an F1-score of 89.23% on the Douban Film Review dataset.
It also achieved 84.84% on the NLPECC dataset, showing strong performance in sentiment detection.
Abstract
In recent years, there has been significant progress in Chinese text sentiment analysis research. However, few studies have investigated the differences between languages, the effectiveness of domain knowledge, and the requirements of downstream tasks. Considering the uniqueness of Chinese text and the practical needs of sentiment analysis, this study aims to address these gaps. To achieve this, we propose a method that deeply integrates the knowledge vector obtained from the emotional knowledge triplets using the TransE model with feature vectors from models like BiGRU and attention mechanisms. We also introduce radical features and emotional part of speech features based on the characteristics of characters and words. In addition, we propose a collaborative approach that integrates characters, words, radicals, and multi-granularity semantic features such as part of speech. Our…
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Taxonomy
TopicsSentiment Analysis and Opinion Mining · Advanced Text Analysis Techniques · Topic Modeling