Paper: Exploiting Domain Knowledge in Aspect Extraction

ACL ID D13-1172
Title Exploiting Domain Knowledge in Aspect Extraction
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013

Aspect extraction is one of the key tasks in sentiment analysis. In recent years, statistical models have been used for the task. However, such models without any domain knowledge often produce aspects that are not interpreta- ble in applications. To tackle the issue, some knowledge-based topic models have been proposed, which allow the user to input some prior domain knowledge to generate coherent aspects. However, existing knowledge-based topic models have several major shortcom- ings, e.g., little work has been done to incor- porate the cannot-link type of knowledge or to automatically adjust the number of topics based on domain knowledge. This paper pro- poses a more advanced topic model, called MC-LDA (LDA with m-set and c-set), to ad- dress these problems, which is based ...