Automatically Labelling Sentiment-Bearing Topics with Descriptive Sentence Labels.

In this paper, we propose a simple yet effective approach for automatically labelling sentiment-bearing topics with descriptive sentence labels. Specifically, our approach consists of two components: (i) a mechanism which can automatically learn the relevance to sentiment-bearing topics of the underlying sentences in a corpus; and (ii) a sentence ranking algorithm for label selection that jointly considers topic-sentence relevance as well as aspect and sentiment co-coverage. To our knowledge, we are the first to study the problem of labelling sentiment-bearing topics. Our experimental results show that our approach outperforms four strong baselines and demonstrates the effectiveness of our sentence labels in facilitating topic understanding and interpretation.