  While the general task of textual sentiment classification has been widely
studied, much less research looks specifically at sentiment between a specified
source and target. To tackle this problem, we experimented with a
state-of-the-art relation extraction model. Surprisingly, we found that despite
reasonable performance, the model's attention was often systematically
misaligned with the words that contribute to sentiment. Thus, we directly
trained the model's attention with human rationales and improved our model
performance by a robust 4~8 points on all tasks we defined on our data sets. We
also present a rigorous analysis of the model's attention, both trained and
untrained, using novel and intuitive metrics. Our results show that untrained
attention does not provide faithful explanations; however, trained attention
with concisely annotated human rationales not only increases performance, but
also brings faithful explanations. Encouragingly, a small amount of annotated
human rationales suffice to correct the attention in our task.
