A Mutually Enhanced Bidirectional Approach for Jointly Mining User Demand and Sentiment (Student Abstract)
DOI:
https://doi.org/10.1609/aaai.v37i13.26999Keywords:
Natural Language Processing, Aspect-based Sentiment Analysis, User Demand MiningAbstract
User demand mining aims to identify the implicit demand from the e-commerce reviews, which are always irregular, vague and diverse. Existing sentiment analysis research mainly focuses on aspect-opinion-sentiment triplet extraction, while the deeper user demands remain unexplored. In this paper, we formulate a novel research question of jointly mining aspect-opinion-sentiment-demand, and propose a Mutually Enhanced Bidirectional Extraction (MEMB) framework for capturing the dynamic interaction among different types of information. Finally, experiments on Chinese e-commerce data demonstrate the efficacy of the proposed model.Downloads
Published
2024-07-15
How to Cite
Mao, X., Qian, H., Yuan, M., & Li, Q. (2024). A Mutually Enhanced Bidirectional Approach for Jointly Mining User Demand and Sentiment (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16278-16279. https://doi.org/10.1609/aaai.v37i13.26999
Issue
Section
AAAI Student Abstract and Poster Program