Abstract
Opinions (reviews) on web resources (e.g., courses, movies), generated by users, become increasingly exploited in text analysis tasks, the detection of contradictory opinions being one of them. This paper focuses on the quantification of sentiment-based contradictions around specific aspects in reviews. However, it is necessary to study the contradictions with respect to the temporal dimension of reviews (their sessions). In general, for web resources such as online courses (e.g. coursera or edX), reviews are often generated during the course sessions. Between sessions, users stop reviewing courses, and there are chances that courses will be updated. So, in order to avoid the confusion of contradictory reviews coming from two or more different sessions, the reviews related to a given resource should be firstly grouped according to their corresponding session. Secondly, aspects are identified according to the distributions of the emotional terms in the vicinity of the most frequent nouns in the reviews collection. Thirdly, the polarity of each review segment containing an aspect is estimated. Then, only resources containing these aspects with opposite polarities are considered. Finally, the contradiction intensity is estimated based on the joint dispersion of polarities and ratings of the reviews containing aspects. The experiments are conducted on the Massive Open Online Courses data set containing 2244 courses and their 73,873 reviews, collected from coursera.org. The results confirm the effectiveness of our approach to find and quantify contradiction intensity.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Badache, I., Boughanem, M.: Social priors to estimate relevance of a resource. In: IIiX, pp. 106–114 (2014)
Badache, I., Boughanem, M.: Fresh and diverse social signals: any impacts on search? In: CHIIR, pp. 155–164 (2017)
De Marneffe, M-C., Rafferty, A., Manning, C.: Finding contradictions in text. In: ACL, vol. 8, pp. 1039–1047 (2008)
Dori-Hacohen, S., Allan, J.: Automated controversy detection on the web. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds.) ECIR 2015. LNCS, vol. 9022, pp. 423–434. Springer, Cham (2015). doi:10.1007/978-3-319-16354-3_46
Ennals, R., Byler, D., Agosta, J.M., Rosario, B.: What is disputed on the web? In: WICOW, pp. 67–74 (2010)
Hamdan, H., Bellot, P., Bechet, F.: Lsislif: Crf and logistic regression for opinion target extraction and sentiment polarity analysis. In: SemEval, pp. 753–758 (2015)
Harabagiu, S., Hickl, A., Lacatusu, F.: Negation, contrast and contradiction in text processing. In: AAAI, vol. 6, pp. 755–762 (2006)
Hassan, A., Abu-Jbara, A., Radev, D.: Detecting subgroups in online discussions by modeling positive and negative relations among participants. In: EMNLP (2012)
Htait, A., Fournier, S., Bellot, P.: Using web search engines for English and Arabic unsupervised sentiment intensity prediction. In: SemEval (2016)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: KDD (2004)
Jang, M., Allan, J.: Improving automated controversy detection on the web. In: SIGIR, pp. 865–868 (2016)
Kim, S., Zhang, J., Chen, Z., Oh, A., Liu, S.: A hierarchical aspect-sentiment model for online reviews. In: AAAI (2013)
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Berkeley Symposium on Mathematical Statistics and Probability (1967)
Mohammad, S.M., Kiritchenko, S., Zhu, X.: Nrc-canada: Building the state-of-the-art in sentiment analysis of tweets. In: SemEval (2013)
Mukherjee, A., Liu, B.: Mining contentions from discussions and debates. In: KDD, pp. 841–849 (2012)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: EMNLP, pp. 79–86 (2002)
Poria, S., Cambria, E., Ku, L., Gui, C., Gelbukh, A.: A rule-based approach to aspect extraction from product reviews. In: SocialNLP (2014)
Qiu, M., Yang, L., Jiang, J.: Modeling interaction features for debate side clustering. In: CIKM, pp. 873–878 (2013)
Socher, R., Perelygin, A., Wu, J.Y., Chuang, J., Manning, C.D., Ng, A.Y., Potts, C.: Recursive deep models for semantic compositionality over a sentiment treebank. In: EMNLP, vol. 1631, p. 1642 (2013)
Titov, I., McDonald, R.: Modeling online reviews with multi-grain topic models. In: WWW, pp. 111–120 (2008)
Tsytsarau, M., Palpanas, T., Denecke, K.: Scalable discovery of contradictions on the web. In: WWW, pp. 1195–1196. ACM (2010)
Tsytsarau, M., Palpanas, T., Denecke, K.: Scalable detection of sentiment-based contradictions. DiversiWeb, WWW (2011)
Turney, P.D.: Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: ACL, pp. 417–424 (2002)
Wang, L., Cardie, C.: A piece of my mind: a sentiment analysis approach for online dispute detection. In: ACL, pp. 693–699 (2014)
Acknowledgement
The project leading to this publication has received funding from Excellence Initiative of Aix-Marseille University - A*MIDEX, a French “Investissements d’Avenir” programme.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Badache, I., Fournier, S., Chifu, AG. (2017). Finding and Quantifying Temporal-Aware Contradiction in Reviews. In: Sung, WK., et al. Information Retrieval Technology. AIRS 2017. Lecture Notes in Computer Science(), vol 10648. Springer, Cham. https://doi.org/10.1007/978-3-319-70145-5_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-70145-5_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70144-8
Online ISBN: 978-3-319-70145-5
eBook Packages: Computer ScienceComputer Science (R0)