Stance Identification by Sentiment and Target Detection

被引:0
|
作者
Chen, Chiao-Chen [1 ]
Tsai, Hsin-Yu [1 ]
Huang, Hen-Hsen [2 ]
Chen, Hsin-Hsi [3 ]
机构
[1] Natl Taiwan Univ, Taipei, Taiwan
[2] Natl Chengchi Univ, MOST Joint Res Ctr AI Technol & All Vista Healthc, Taipei, Taiwan
[3] Natl Taiwan Univ, MOST Joint Res Ctr AI Technol & All Vista Healthc, Taipei, Taiwan
关键词
stance detection; sentiment analysis; capsule network;
D O I
10.1109/WIIAT50758.2020.00047
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Stance detection has attracted attention for several years. Previous work focuses mainly on a supervised topic-specific setting which requires labeled data for each individual topic. In this paper, we discuss the characteristics of different types of topics, and the interaction among sentiment, target, and stance in a sentence. We propose an approach without the need of stance-labeled data to identify stance incorporating the findings of their interaction. The proposed approach is topic independent and can be applied to individual topics flexibly. Furthermore, we evaluate our method on the SemEval-2016 dataset for detecting stance in tweets, which contains six topics of two different types. Experimental results show that our approach is promising even when stance-labeled data is not available.
引用
收藏
页码:331 / 338
页数:8
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