A Mutually Enhanced Bidirectional Approach for Jointly Mining User Demand and Sentiment (Student Abstract)

Xue Mao, Haoda Qian, Minjie Yuan, Qiudan Li

AAAI 2023 pp. 16278-16279

doi:10.1609/AAAI.V37I13.26999 /aaai/2023/mao2023aaai-mutually/

Abstract

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.

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Cite

Text

Mao et al. "A Mutually Enhanced Bidirectional Approach for Jointly Mining User Demand and Sentiment (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I13.26999

Markdown

[Mao et al. "A Mutually Enhanced Bidirectional Approach for Jointly Mining User Demand and Sentiment (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/mao2023aaai-mutually/) doi:10.1609/AAAI.V37I13.26999

BibTeX

@inproceedings{mao2023aaai-mutually,
  title     = {{A Mutually Enhanced Bidirectional Approach for Jointly Mining User Demand and Sentiment (Student Abstract)}},
  author    = {Mao, Xue and Qian, Haoda and Yuan, Minjie and Li, Qiudan},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2023},
  pages     = {16278-16279},
  doi       = {10.1609/AAAI.V37I13.26999},
  url       = {https://mlanthology.org/aaai/2023/mao2023aaai-mutually/}
}