Abstract
Customer reviews feature opinions or sentiments that a review writer has given, and these opinions or sentiments have an impact on the reader. Identifying and presenting word associations that indicate a sentiment orientation and semantics can aid in selecting the best review for providing the information customers are seeking. In this paper, we attempted to discover the context structure and the context path presenting explicit semantics in review texts. To this end, we extracted word co-occurrences and converted them to a cosine adjacency matrix. Then a co-word network applied by Pathfinder scaling was constructed. Finally, we measured the context score and presented context paths from the context structure in the review texts. In results, our approach found that a compound noun is easy to detect by network analysis. The extracted context paths remain intact, a sentiment polarity derived from review texts. The evaluative expression for a certain aspect of a product or service is clearer and more specified within the context path. Furthermore, it is not necessary to train reference words to detect the sentiment orientations.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Ghose, A., Ipeirotis, P.G.: Designing novel review ranking systems: predicting the usefulness and impact of reviews. In: 9th International Conference on Electronic Commerce, pp. 303–310. ACM (2007)
Hu, X., Downie, J.S., West, K., Ehmann, A.: Mining music reviews: promising preliminary results. In: 6th International Symposium on Music Information Retrieval (2005)
Titov, I., McDonald, R.: Modeling online reviews with multi-grain topic models. In: 17th International Conference on World Wide Web, pp. 111–120. ACM (2008)
Hatzivassiloglou, V., McKeown, K.R.: Predicting the semantic orientation of adjectives. In: 35th Annual Meeting of the Association for Computational Linguistics and 8th Conference of the European Chapter of the Association for Computational Linguistics, Association for Computational Linguistics, pp. 174–181 (1997)
Turney, P.D.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: 40th Annual Meeting on Association for Computational Linguistics, Association for Computational Linguistics, pp. 417–424 (2002)
Turney, P.D., Littman, M.L.: Measuring praise and criticism: inference of semantic orientation from association. ACM Trans. Inf. Syst. (TOIS) 21(4), 315–346 (2003)
Xia, R., Xu, F., Yu, J., Qi, Y., Cambria, E.: Polarity shift detection, elimination and ensemble: a three-stage model for document-level sentiment analysis. Inf. Process. Manag. 52(1), 36–45 (2016)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: ACL 2002 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, vol. 10, pp. 79–86 (2002)
Ye, Q., Zhang, Z., Law, R.: Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert Syst. Appl. 36(3), 6527–6535 (2009)
Dave, K., Lawrence, S., Pennock, D.M.: Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In: 12th International Conference on World Wide Web, pp. 519–528. ACM (2003)
Jin, W., Ho, H.H., Srihari, R.K.: OpinionMiner: a novel machine learning system for web opinion mining and extraction. In: 15th ACM SIGKDD, pp. 1195–1204. ACM (2009)
Choi, Y., Cardie, C., Riloff, E., Patwardhan, S.: Identifying sources of opinions with conditional random fields and extraction patterns. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, Association for Computational Linguistics, pp. 355–362 (2005)
Schvaneveldt, R.W., Durso, F.T., Dearholt, D.W.: Network structures in proximity data. Psychol. Learn. Motiv. 24, 249–284 (1989)
Mei, Q., Ling, X., Wondra, M., Su, H., Zhai, C.: Topic sentiment mixture: modeling facets and opinions in weblogs. In: 16th International Conference on World Wide Web, pp. 171–180. ACM (2007)
Jo, Y., Oh, A.H.: Aspect and sentiment unification model for online review analysis. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 815–824. ACM (2011)
Börner, K., Chen, C., Boyack, K.W.: Visualizing knowledge domains. Annu. Rev. Inf. Sci. Technol. 37(1), 179–255 (2003)
McCain, K.W.: The structure of biotechnology R&D. Scientometrics 32(2), 153–175 (1995)
Chen, C.: Visualising semantic spaces and author co-citation networks in digital libraries. Inf. Process. Manag. 35(3), 401–420 (1999)
White, H.D.: Pathfinder networks and author cocitation analysis: a remapping of paradigmatic information scientists. J. Am. Soc. Inf. Sci. Technol. 54(5), 423–434 (2003)
White, H.D., McCain, K.W.: Visualizing a discipline: an author co-citation analysis of information science, 1972–1995. J. Am. Soc. Inf. Sci. 49(4), 327–355 (1998)
Sun, J., Tang, J.: A survey of models and algorithms for social influence analysis. In: Social Network Data Analytics, pp. 177–214 (2011)
Acknowledgements
This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2015S1A3A2046711).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Kim, E.HJ., Kim, S. (2016). An Effective Approach to Finding a Context Path in Review Texts Using Pathfinder Scaling. In: Spiro, E., Ahn, YY. (eds) Social Informatics. SocInfo 2016. Lecture Notes in Computer Science(), vol 10046. Springer, Cham. https://doi.org/10.1007/978-3-319-47880-7_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-47880-7_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47879-1
Online ISBN: 978-3-319-47880-7
eBook Packages: Computer ScienceComputer Science (R0)