Abstract
On the online reviews, one of the important types of information is the sentiment explanation which expresses a content users generated. Sentiment explanation is a sentence that expresses detailed reason of sentiment (i.e., “explanatoriness”) and plays an important role in opinion summarization. In this paper, we propose and study a method for scoring the explanatoriness of a sentence. A first method is to adapt an existing method and a second method based on a probabilistic model. Experimental results show that the proposed methods are effective, presenting a better value for a state of the art sentence ranking method for standard text summarization.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kim, H.D., Castellanos, M.G., Hsu, M., Zhai, C., Dayal, U., Ghosh, R.: Ranking explanatory sentences for opinion summarization. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1069–1072. SIGIR ’13 (2013)
Robertson, S., Walker, S.: Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval. In: ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pp. 345–354 (1994)
Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240. WSDM ’08 (2008)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177. KDD ’04 (2004)
Jones, K.S., Walker, S., Robertson, S.E.: A probabilistic model of information retrieval: development and comparative experiments. In: Information Processing and Management, pp. 779–808 (2000)
Okapi BM25: a non-binary model. http://nlp.stanford.edu/IR-book/html/htmledition/okapi-bm25-a-non-binary-model-1.html
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Vo, T.T., Le, B., Le Nguyen, M. (2016). Scoring Explanatoriness of a Sentence and Ranking for Explanatory Opinion Summary. In: Król, D., Madeyski, L., Nguyen, N. (eds) Recent Developments in Intelligent Information and Database Systems. Studies in Computational Intelligence, vol 642. Springer, Cham. https://doi.org/10.1007/978-3-319-31277-4_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-31277-4_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-31276-7
Online ISBN: 978-3-319-31277-4
eBook Packages: EngineeringEngineering (R0)