Stance classification with target-specific neural attention

Research output: Chapter in Book/Report/Conference proceedingConference contribution

View graph of relations Save citation

Authors

Research units

Abstract

Classifying stance expressed in a text toward specific target is an emerging problem in opinion mining. A major difference between stance detection and traditional aspect-level sentiment classification is that the target of the stance might not be explicitly mentioned in text. In this paper, we show that the stance polarity of a text is not merely dependent on the content but is also highly determined by the concerned target. To this end, We propose a neural network based model, which incorporate target-specific information into stance classification using a novel attention mechanism. The proposed attention mechanism can focus on critical parts of a text. We evaluate our model on the SemEval 2016 Task 6 Twitter Stance Detection corpus achieving satisfactory performance. Our model achieves significant and consistent improvements on this task as compared with baselines.

Request a copy

Request a copy

Details

Publication date23 May 2017
Publication titleIJCAI 2017 proceedings
Original languageEnglish
Event26th International Joint Conference on Artificial Intelligence (IJCAL 2017) - Melbourne, Australia

Conference

Conference26th International Joint Conference on Artificial Intelligence (IJCAL 2017)
CountryAustralia
CityMelbourne
Period19/08/1725/08/17

Bibliographic note

-

Employable Graduates; Exploitable Research

Copy the text from this field...