Stance Detection in Code-Mixed Hindi-English Social Media Data using Multi-Task Learning

Sushmitha Reddy Sane, Suraj Tripathi, Koushik Reddy Sane, Radhika Mamidi


Abstract
Social media sites like Facebook, Twitter, and other microblogging forums have emerged as a platform for people to express their opinions and views on different issues and events. It is often observed that people tend to take a stance; in favor, against or neutral towards a particular topic. The task of assessing the stance taken by the individual became significantly important with the emergence in the usage of online social platforms. Automatic stance detection system understands the user’s stance by analyzing the standalone texts against a target entity. Due to the limited contextual information a single sentence provides, it is challenging to solve this task effectively. In this paper, we introduce a Multi-Task Learning (MTL) based deep neural network architecture for automatically detecting stance present in the code-mixed corpus. We apply our approach on Hindi-English code-mixed corpus against the target entity - “Demonetisation.” Our best model achieved the result with a stance prediction accuracy of 63.2% which is a 4.5% overall accuracy improvement compared to the current supervised classification systems developed using the benchmark dataset for code-mixed data stance detection.
Anthology ID:
W19-1301
Volume:
Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Month:
June
Year:
2019
Address:
Minneapolis, USA
Venue:
WASSA
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–5
Language:
URL:
https://aclanthology.org/W19-1301
DOI:
10.18653/v1/W19-1301
Bibkey:
Cite (ACL):
Sushmitha Reddy Sane, Suraj Tripathi, Koushik Reddy Sane, and Radhika Mamidi. 2019. Stance Detection in Code-Mixed Hindi-English Social Media Data using Multi-Task Learning. In Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 1–5, Minneapolis, USA. Association for Computational Linguistics.
Cite (Informal):
Stance Detection in Code-Mixed Hindi-English Social Media Data using Multi-Task Learning (Sane et al., WASSA 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-1301.pdf