Abstract
Solving Stance Detection on Tweets as Multi-Domain and Multi-Task Text Classification
Highlights
Stance detection [1, 2] is the task of identifying the attitude conveyed in the text towards a given target
We novelly formulate the task as a multidomain multi-task learning problem
We employ the shared-private structure in stance detection for the first time to fully exploit the shared stance features across different targets
Summary
Stance detection [1, 2] is the task of identifying the attitude conveyed in the text towards a given target. It is useful for various opinion mining tasks, especially important for understanding peoples’ opinions expressed towards targets of interest on social media platforms. Unlike conventional aspectlevel sentiment classification, the target may not appear in the text. It is crucial to decide the relationship between the opinioned entity and the given target to accurately understanding the stance. People tend to use different expressions towards different targets, which makes it difficult for a single model to learn diverse stance features
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