Customer product review summarization over time for competitive intelligence

• Autorzy: Amarouche Kamal , Benbrahim Houda , Kassou Ismail

Identyfikatory

DOI

10.14313/JAMRIS_4-2018/28

• Języki publikacji: EN

Abstrakty

EN

Nowadays, Customer’s product reviews can be widely found on the Web, be it in personal blogs, forums, or ecommerce websites. They contain important products’ information and therefore became a new data source for competitive intelligence. On that account, these reviews need to be analyzed and summarized in order to help the leader of an entity (company, brand, etc.) to make appropriate decisions in an efective way. However, most previous review summarization studies focus on summarizing sentiment distribution toward different product features without taking into account that the real advantages and disadvantages of a product clarify over time. For this reason, in this work we aim to propose a new system for product opinion summarization which depends on the time when reviews are expressed and that covers the sentiments change about product features. The proposed system firstly, generates a summary based on product features in order to give more accurate and efficient information about different features. secondly, classify the product based on its features in its appropriate class (good, medium or bad product) using a fuzzy logic system. The experimental results demonstrate the effectiveness of the proposed system to generate the real image of a product and its features in reviews.

Słowa kluczowe

EN

feature extraction; fuzzy logic; competitive intelligence; opinion mining; opinion summarization; sentiment analysis; SentiWordNet

PL

ekstrakcja cech; logika rozmyta; wywiad konkurencyjny; eksploracja opinii; podsumowanie opinii; analiza nastrojów; SentiWordNet

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2018
• Tom: Vol. 12, No. 4
• Strony: 70--82
• Opis fizyczny: Bibliogr. 32 poz., rys.

Twórcy

autor

Amarouche Kamal

• ALBIRONI Research Team, ENSIAS, Mohammed 5 University, Rabat, Morocco

autor

Benbrahim Houda

• ALBIRONI Research Team, ENSIAS, Mohammed 5 University, Rabat, Morocco

autor

Kassou Ismail

• ALBIRONI Research Team, ENSIAS, Mohammed 5 University, Rabat, Morocco

Bibliografia

• [1] L. Abd-Elhamid, D. Elzanfaly, and A. S. Eldin, “Feature-based sentiment analysis in online Arabic reviews”. In: 2016 11th International Conference on Computer Engineering Systems (ICCES), 2016, 260–265, 10.1109/ICCES.2016.7822011.
• [2] M. Abulaish, Jahiruddin, M. N. Doja, and T. Ahmad, Feature and Opinion Mining for Customer Review Summarization”. In: S. Chaudhury, S. Mitra, C. A. Murthy, P. S. Sastry, and S. K. Pal, eds., Pattern Recognition and Machine Intelligence, 2009, 219–224.
• [3] K. Amarouche, H. Benbrahim, and I. Kassou, “Product Opinion Mining for Competitive Intelligence”, Procedia Computer Science, vol. 73, 2015, 358–365, 10.1016/j.procs.2015.12.004.
• [4] K. Amarouche, H. Benbrahim, and I. Kassou, “Product Features Extraction From Opinions According To Time”, 2016, 10.5281/zenodo.1125349.
• [5] E. Asgarian and M. Kahani, “Designing an Integrated Semantic Framework for Structured Opinion Summarization”. In: V. Presutti, C. d’Amato, F. Gandon, M. d’Aquin, S. Staab, and A. Tordai, eds., The Semantic Web: Trends and Challenges, 2014, 885–894.
• [6] K. Bafna and D. Toshniwal, “Feature based Summarization of Customers’ Reviews of Online Products”, Procedia Computer Science, vol. 22, 2013, 142–151, 10.1016/j.procs.2013.09.090.
• [7] P. Boltzheim, B. Hamori, and L. T. Kóczy, “Optimization of trapezoidal membership functions in a fuzzy rule system by the bacterial algorithm approach”, Budapest University of Telecommunications and Economics, Department of Telecommunications and Telematics, Budapest, 2001.
• [8] S. Chatterji, N. Varshney, and R. K. Rahul, “AspectFrameNet: a frameNet extension for analysis of sentiments around product aspects”, The Journal of Supercomputing, vol. 73, no. 3, 2017, 961–972, 10.1007/s11227-016-1808-6.
• [9] J. Chen, D. P. Huang, S. Hu, Y. Liu, Y. Cai, and H. Min, “An opinion mining framework for Cantonese reviews”, Journal of Ambient Intelligence and Humanized Computing, vol. 6, no. 5, 2015, 541–547, 10.1007/s12652-014-0237-8.
• [10] S. G. Cho and S. B. Kim, “Feature network-driven quadrant mapping for summarizing customer reviews”, Journal of Systems Science and Systems Engineering, vol. 26, no. 5, 2017, 646–664, 10.1007/s11518-017-5329-5.
• [11] M. Daneshvar, “Programmable Trapezoidal and Gaussian Membership Function Generator”, 2012.
• [12] A. Esuli and F. Sebastiani, “Determining Term Subjectivity and Term Orientation for Opinion Mining”, 2006.
• [13] M. Hu and B. Liu, “Mining and Summarizing Customer Reviews”. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, 2004, 168–177, 10.1145/1014052.1014073, event-place: Seattle, WA, USA.
• [14] A. Kangale, S. K. Kumar, M. A. Naeem, M. Williams, and M. K. Tiwari, “Mining consumer reviews to generate ratings of different product attributes while producing feature-based review-summary”, International Journal of Systems Science, vol. 47, no. 13, 2016, 3272–3286, 10.1080/00207721.2015.1116640.
• [15] H. Kansal and D. Toshniwal, “Aspect based Summarization of Context Dependent Opinion Words”, Procedia Computer Science, vol. 35, 2014, 166–175, 10.1016/j.procs.2014.08.096.
• [16] F. H. Khan, U. Qamar, and S. Bashir, “SentiMI: Introducing point-wise mutual information with SentiWordNet to improve sentiment polarity detection”, Applied Soft Computing, vol. 39, 2016, 140–153, 10.1016/j.asoc.2015.11.016.
• [17] P. P. Ładyżyński and P. Grzegorzewski, “A Recommender System Based on Customer Reviews Mining”. In: L. Rutkowski, M. Korytkowski, R. Scherer, R. Tadeusiewicz, L. A. Zadeh, and J. M. Zurada, eds., Artifiicial Intelligence and Soft Computing, 2014, 512–523.
• [18] W. V. Leekwijck and E. E. Kerre, “Defuzzification: criteria and classification”, Fuzzy Sets and Systems, vol. 108, no. 2, 1999, 159–178,10.1016/S0165-0114(97)00337-0.
• [19] Y. Li, Z. Qin, W. Xu, and J. Guo, “A holistic model of mining product aspects and associated sentiments from online reviews”, Multimedia Tools and Applications, vol. 74, no. 23, 2015, 10177–10194, 10.1007/s11042-014-2158-0.
• [20] B. Liu, M. Hu, and J. Cheng, “Opinion Observer: Analyzing and Comparing Opinions on the Web”. In: Proceedings of the 14th International Conference on World Wide Web, New York, NY, USA, 2005, 342–351, 10.1145/1060745.1060797, event-place: Chiba, Japan.
• [21] A. Maisto and S. Pelosi, “Feature-Based Customer Review Summarization”. In: R. Meersman, H. Panetto, A. Mishra, R. Valencia-Garcı́a, A. L. Soares, I. Ciuciu, F. Ferri, G. Weichhart, T. Moser, M. Bezzi, and H. Chan, eds., On the Move to Meaningful Internet Systems: OTM 2014 Workshops, 2014, 299–308.
• [22] I. Mohammed, B. D. Guillet, and R. Law, “Competitor set identification in the hotel industry: A case study of a full-service hotel in Hong Kong”, International Journal of Hospitality Management, vol. 39, 2014, 29–40, 10.1016/j.ijhm.2014.02.002.
• [23] H. Rahimi and H. E. Bakkali, “CIOSOS: Combined Idiomatic-Ontology Based Sentiment Orientation System for Trust Reputation in Ecommerce”. In: Á . Herrero, B. Baruque, J. Sedano,H. Quintián, and E. Corchado, eds., International Joint Conference, 2015, 189–200.
• [24] N. Shuyo. “Language Detection with Infinitygram. Contribute to shuyo/ldig development by creating an account on GitHub”, April 2019. original-date: 2011-11-18T01:18:37Z.
• [25] L. Štefániková and G. Masárová, “The Need of Complex Competitive Intelligence”, Procedia – Social and Behavioral Sciences, vol. 110, 2014, 669–677, 10.1016/j.sbspro.2013.12.911.
• [26] D. Wang, S. Zhu, and T. Li, “SumView: A Webbased nengine for summarizing product reviews and customer opinions”, Expert Systems with Applications, vol. 40, no. 1, 2013, 27–33,10.1016/j.eswa.2012.05.070.
• [27] Y. Wu, Q. Zhang, X. Huang, and L. Wu, “Phrase Dependency Parsing for Opinion Mining”. In:Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume3 - Volume 3, Stroudsburg, PA, USA, 2009, 1533–1541, event-place: Singapore.
• [28] K. Xu, S. S. Liao, J. Li, and Y. Song, “Mining comparative opinions from customer reviews for Competitive Intelligence”, Decision Support Systems, vol. 50, no. 4, 2011, 743–754, 10.1016/j.dss.2010.08.021.
• [29] Y. Yang, C. Chen, and F. S. Bao, “Aspect-Based Helpfulness Prediction for Online Product Reviews”.In: 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), 2016, 836–843, 10.1109/ICTAI.2016.0130.
• [30] X. Yuan, N. Sa, G. Begany, and H. Yang, “What Users Prefer and Why: A User Study on Effective Presentation Styles of Opinion Summarization”. In: J. Abascal, S. Barbosa, M. Fetter, T. Gross, P. Palanque, and M. Winckler, eds., Human-Computer Interaction – INTERACT 2015, 2015, 249–264.
• [31] X. Zhou, X. Wan, and J. Xiao, “CMiner: Opinion Extraction and Summarization for Chinese Microblogs”, IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 7, 2016, 1650–1663, 10.1109/TKDE.2016.2541148.
• [32] L. Zhuang, F. Jing, and X.-Y. Zhu, “Movie Review Mining and Summarization”. In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management, New York, NY, USA, 2006, 43–50, 10.1145/1183614.1183625, event-place: Arlington, Virginia, USA.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).