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- Title
A Multi-Stance Detection Method by Fusing Sentiment Features.
- Authors
Huang, Weidong; Yang, Jinyuan
- Abstract
Stance information has a significant influence on market strategy, government policy, and public opinion. Users differ not only in their polarity but also in the degree to which they take a stand. The traditional classification of stances is quite simple and cannot fully depict the diversity of stances. At the same time, traditional approaches ignore user sentiment features when expressing their stances. As a result, this paper develops a multi-stance detection model by fusing sentiment features. First, a five-category stance indicator system is built based on the LDA model, then sentiment features are extracted from the reviews using the sentiment lexicon, and finally, stance detection is implemented using a hybrid neural network model. The experiment shows that the proposed method can classify stances into five categories and perform stance detection more accurately.
- Subjects
ARTIFICIAL neural networks; FEATURE extraction; MARKETING strategy; PUBLIC opinion; GOVERNMENT policy
- Publication
Applied Sciences (2076-3417), 2024, Vol 14, Issue 9, p3916
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app14093916