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.
|Name||Lecture Notes in Computer Science|
|Conference||22nd International Conference on Natural Language & Information Systems|
|Abbreviated title||NLDB 2017|
|Period||21/06/17 → 23/06/17|