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The topical stance detection problem addresses detecting the stance of thetext content with respect to a given topic: whether the sentiment of the giventext content is in FAVOR of (positive), is AGAINST (negative), or is NONE(neutral) towards the given topic. Using the concept of attention, we develop atwo-phase solution. In the first phase, we classify subjectivity - whether agiven tweet is neutral or subjective with respect to the given topic. In thesecond phase, we classify sentiment of the subjective tweets (ignoring theneutral tweets) - whether a given subjective tweet has a FAVOR or AGAINSTstance towards the topic. We propose a Long Short-Term memory (LSTM) based deepneural network for each phase, and embed attention at each of the phases. Onthe SemEval 2016 stance detection Twitter task dataset, we obtain a best-casemacro F-score of 68.84% and a best-case accuracy of 60.2%, outperforming theexisting deep learning based solutions. Our framework, T-PAN, is the first inthe topical stance detection literature, that uses deep learning within atwo-phase architecture.

Topical Stance Detection for Twitter: A Two-Phase LSTM Model Using Attention