We use the sentiment lexicon 1 by Hu and Liu (2004) and construct a stance lexicon 2 (of almost 2,000 words) with words related to the five targets in the SemEval-2016 dataset.
we construct a stance lexicon for each target from the training data available from the Se-mEval 2016 dataset and from an extra 1,000 tweets for each target that we collected from Twitter using specific hashtags.