Exploring deep neural networks for multitarget stance detection. (6th August 2018)

• Record Type: Journal Article
• Authors: Sobhani, Parinaz; Inkpen, Diana; Zhu, Xiaodan
• Is Part Of: Computational intelligence. Volume 35:Number 1(2019)
• Page Start: 82
• Page End: 97
• Publication Date: 2018-08-06
• Subjects: deep neural networks -- LSTM -- multitarget -- sentiment analysis -- stance detection; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12189
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: Detecting subjectivity expressed toward concerned targets is an interesting problem and has received intensive study. Previous work often treated each target independently, ignoring the potential (sometimes very strong) dependency that could exist among targets (eg, the subjectivity expressed toward two products or two political candidates in an election). In this paper, we relieve such an independence assumption in order to jointly model the subjectivity expressed toward multiple targets. We propose and show that an attention‐based encoder‐decoder framework is very effective for this problem, outperforming several alternatives that jointly learn dependent subjectivity through cascading classification or multitask learning, as well as models that independently predict subjectivity toward individual targets.