A key question in sentiment analysis is whether sentiment ex-pressions, in a given text, are related to particular entities. This is an imperative question, since people are typically interested in sentiments on specific entities and not in the overall sentiment articulated in an article or a document. Sentiment relevance is aimed at addressing this precise problem. In this paper, we argue that exploiting information about the focus of the document on the entity of interest can significantly improve the task of detecting sentiment relevance and, hence, the final sentiment scores assigned for the entities. In order to assess the value of such information, we look at various methods for detecting sentiment relevance for entities. We consider both rule-based algorithms that rely on the entity's physical or syntactic proximity to the sentiment expressions as well as more sophisticated machine learning classification algorithms. We demonstrate that the focus of the document on the entities within it is, indeed, an important piece of information, which can be accurately learned with super-vised classification means. We, further, found that overall classification-based algorithms perform better than the deterministic ones in identifying sentiment relevance, with sequence-classification performing significantly better than direct classification.
|Original language||American English|
|Title of host publication||Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015|
|Editors||Xindong Wu, Alexander Tuzhilin, Hui Xiong, Jennifer G. Dy, Charu Aggarwal, Zhi-Hua Zhou, Peng Cui|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||10|
|State||Published - 29 Jan 2016|
|Event||15th IEEE International Conference on Data Mining Workshop, ICDMW 2015 - Atlantic City, United States|
Duration: 14 Nov 2015 → 17 Nov 2015
|Name||Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015|
|Conference||15th IEEE International Conference on Data Mining Workshop, ICDMW 2015|
|Period||14/11/15 → 17/11/15|
Bibliographical noteFunding Information:
This research was supported by The Israel Science Foundation (grant No. 906/13),by the Israel Ministry of Science and Technology Center of Knowledge in Machine Learning and Artificial Intelligence, by the Israel Ministry of Defense/MAFAT and by the Intel Collaborative Research Institutes.
© 2015 IEEE.
- Document Type with Respect to Entity
- Document-level Infor-mation
- Entity-level Sentiment Analysis
- Focus of the Document
- Sentiment Analysis
- Sentiment Relevance