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