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Electronic Commerce Research

Erschienen in:

14.02.2022

A novel approach for product competitive analysis based on online reviews

verfasst von: Zhen He, Lu Zheng, Shuguang He

Erschienen in: Electronic Commerce Research | Ausgabe 4/2023

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Abstract

Recently, online reviews have become a prevalent information source for competitive analysis because they provide rich information on the voices of customers. Based on online reviews, we propose a novel method named Integrated-Degree based K-shell decomposition (ID-KS) to conduct competitive analysis via product comparison networks. Under the consideration of feature differences among products, we apply text-mining approaches and ID-KS to convert online reviews into competitive insights including competitor identification, product comparison, product ranking, brand comparison and market-structure analysis. To validate the feasibility and the effectiveness of ID-KS, we demonstrate our approach in two cases, SUV cars and laptops, and compare it with state-of-the-art methods. The results show that ID-KS analyzes product comparison networks more effectively and properly, and it derives comprehensive comparative insights that are not fully captured by existing studies.

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Gao, S., Tang, O., Wang, H., & Yin, P. (2018). Identifying competitors through comparative relation mining of online reviews in the restaurant industry. International Journal of Hospitality Management, 71, 19–32. https://doi.org/10.1016/j.ijhm.2017.09.004CrossRef

Nasr, S. B., Bécan, G., Acher, M., Ferreira Filho, J. B., Sannier, N., Baudry, B., & Davril, J.-M. (2017). Automated extraction of product comparison matrices from informal product descriptions. Journal of Systems and Software, 124, 82–103. https://doi.org/10.1016/j.jss.2016.11.018CrossRef

Liu, Y., Bi, J.-W., & Fan, Z.-P. (2017). Ranking products through online reviews: A method based on sentiment analysis technique and intuitionistic fuzzy set theory. Information Fusion, 36, 149–161. https://doi.org/10.1016/j.inffus.2016.11.012CrossRef

Amarouche, K., Benbrahim, H., & Kassou, I. (2015). Product opinion mining for competitive intelligence. Procedia Computer Science, 73, 358–365. https://doi.org/10.1016/j.procs.2015.12.004CrossRef

Kim, S. G., & Kang, J. (2018). Analyzing the discriminative attributes of products using text mining focused on cosmetic reviews. Information Processing & Management, 54(6), 938–957. https://doi.org/10.1016/j.ipm.2018.06.003CrossRef

Farhadloo, M., Patterson, R. A., & Rolland, E. (2016). Modeling customer satisfaction from unstructured data using a Bayesian approach. Decision Support Systems, 90, 1–11. https://doi.org/10.1016/j.dss.2016.06.010CrossRef

Li, S., Zha, Z.-J., Ming, Z., Wang, M., Chua, T.-S., Guo, J., & Xu, W. (2011). Product comparison using Comparative Relations. Presented at the SIGIR ’11 Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, Beijing, China.

Zhang, Z., Guo, C., & Goes, P. (2013). Product comparison networks for competitive analysis of online word-of-mouth. ACM Transactions on Management Information Systems, 3(4), 1–22. https://doi.org/10.1145/2407740.2407744CrossRef

Yang, X., Yang, G., & Wu, J. (2016). Integrating rich and heterogeneous information to design a ranking system for multiple products. Decision Support Systems, 84, 117–133. https://doi.org/10.1016/j.dss.2016.02.009CrossRef

10.

Tkachenko, M., & Lauw, H. W. (2017). Comparative relation generative model. IEEE Transactions on Knowledge and Data Engineering, 29(4), 771–783. https://doi.org/10.1109/tkde.2016.2640281CrossRef

11.

Kumar, A., & Abirami, S. (2018). Aspect-based opinion ranking framework for product reviews using a Spearman’s rank correlation coefficient method. Information Sciences, 460–461, 23–41. https://doi.org/10.1016/j.ins.2018.05.003CrossRef

12.

Wang, Z., Zhao, Y., Xi, J., & Du, C. (2016). Fast ranking influential nodes in complex networks using a k-shell iteration factor. Physica A: Statistical Mechanics and its Applications, 461, 171–181. https://doi.org/10.1016/j.physa.2016.05.048CrossRef

13.

Wang, W. M., Wang, J. W., Li, Z., Tian, Z. G., & Tsui, E. (2019). Multiple affective attribute classification of online customer product reviews: A heuristic deep learning method for supporting Kansei engineering. Engineering Applications of Artificial Intelligence, 85, 33–45. https://doi.org/10.1016/j.engappai.2019.05.015CrossRef

14.

Zhang, L., Chu, X., & Xue, D. (2018). Identification of the to-be-improved product features based on online reviews for product redesign. International Journal of Production Research, 57(8), 2464–2479. https://doi.org/10.1080/00207543.2018.1521019CrossRef

15.

Wang, W. M., Li, Z., Tian, Z. G., Wang, J. W., & Cheng, M. N. (2018). Extracting and summarizing affective features and responses from online product descriptions and reviews: A Kansei text mining approach. Engineering Applications of Artificial Intelligence, 73, 149–162. https://doi.org/10.1016/j.engappai.2018.05.005CrossRef

16.

Candi, M., Jae, H., Makarem, S., & Mohan, M. (2017). Consumer responses to functional, aesthetic and symbolic product design in online reviews. Journal of Business Research, 81, 31–39. https://doi.org/10.1016/j.jbusres.2017.08.006CrossRef

17.

Jin, J., Ji, P., Liu, Y., & Johnson Lim, S. C. (2015). Translating online customer opinions into engineering characteristics in QFD: A probabilistic language analysis approach. Engineering Applications of Artificial Intelligence, 41, 115–127. https://doi.org/10.1016/j.engappai.2015.02.006CrossRef

18.

Abrahams, A. S., Jiao, J., Wang, G. A., & Fan, W. (2012). Vehicle defect discovery from social media. Decision Support Systems, 54(1), 87–97. https://doi.org/10.1016/j.dss.2012.04.005CrossRef

19.

Zhang, X., Niu, S., Zhang, D., Wang, G. A., & Fan, W. (2015). Predicting Vehicle Recalls with User-Generated Contents: A Text Mining Approach. Presented at the Intelligence and Security Informatics: Pacific Asia Workshop, PAISI 2015, Ho Chi Minh City, Vietnam.

20.

Law, D., Gruss, R., & Abrahams, A. S. (2017). Automated defect discovery for dishwasher appliances from online consumer reviews. Expert Systems with Applications, 67, 84–94. https://doi.org/10.1016/j.eswa.2016.08.069CrossRef

21.

Mummalaneni, V., Gruss, R., Goldberg, D. M., Ehsani, J. P., & Abrahams, A. S. (2018). Social media analytics for quality surveillance and safety hazard detection in baby cribs. Safety Science, 104, 260–268. https://doi.org/10.1016/j.ssci.2018.01.003CrossRef

22.

Khare, V. R., & Chougule, R. (2012). Decision support for improved service effectiveness using domain aware text mining. Knowledge-Based Systems, 33, 29–40. https://doi.org/10.1016/j.knosys.2012.03.005CrossRef

23.

Korfiatis, N., Stamolampros, P., Kourouthanassis, P., & Sagiadinos, V. (2019). Measuring service quality from unstructured data: A topic modeling application on airline passengers’ online reviews. Expert Systems with Applications, 116, 472–486. https://doi.org/10.1016/j.eswa.2018.09.037CrossRef

24.

Su, Y., & Teng, W. (2018). Contemplating museums’ service failure: Extracting the service quality dimensions of museums from negative on-line reviews. Tourism Management, 69, 214–222. https://doi.org/10.1016/j.tourman.2018.06.020CrossRef

25.

Du, J., Xu, H., & Huang, X. (2014). Box office prediction based on microblog. Expert Systems with Applications, 41(4), 1680–1689. https://doi.org/10.1016/j.eswa.2013.08.065CrossRef

26.

Hur, M., Kang, P., & Cho, S. (2016). Box-office forecasting based on sentiments of movie reviews and Independent subspace method. Information Sciences, 372, 608–624. https://doi.org/10.1016/j.ins.2016.08.027CrossRef

27.

Hua Li, Sourav S Bhowmick, & Aixin Sun. (2010). Affinity-Driven Prediction and Ranking of Products in Online Product Review Sites. Presented at the Proceedings of the 19th ACM Conference on Information and Knowledge Management, Toronto, Ontario, Canada.

28.

Wang, H., & Wang, W. (2014). Product weakness finder: An opinion-aware system through sentiment analysis. Industrial Management & Data Systems, 114(8), 1301–1320. https://doi.org/10.1108/imds-05-2014-0159CrossRef

29.

Wang, W., Feng, Y., & Dai, W. (2018). Topic analysis of online reviews for two competitive products using latent Dirichlet allocation. Electronic Commerce Research and Applications, 29, 142–156. https://doi.org/10.1016/j.elerap.2018.04.003CrossRef

30.

Jin, J., Ji, P., & Gu, R. (2016). Identifying comparative customer requirements from product online reviews for competitor analysis. Engineering Applications of Artificial Intelligence, 49, 61–73. https://doi.org/10.1016/j.engappai.2015.12.005CrossRef

31.

Wasserman, S., & Faust, K. (1994). Social network analysis: Mthods and applications. Cambridge University Press.

32.

Kitsak, M., Gallos, L. K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H. E., & Makse, H. A. (2010). Identification of influential spreaders in complex networks. Nature Physics, 6(11), 888–893. https://doi.org/10.1038/nphys1746CrossRef

33.

Garas, A., Schweitzer, F., & Havlin, S. (2012). A k-shell decomposition method for weighted networks. New Journal of Physics, 14(8), 083030. https://doi.org/10.1088/1367-2630/14/8/083030CrossRef

34.

Wei, B., Liu, J., Wei, D., Gao, C., & Deng, Y. (2015). Weighted k-shell decomposition for complex networks based on potential edge weights. Physica A: Statistical Mechanics and its Applications, 420, 277–283. https://doi.org/10.1016/j.physa.2014.11.012CrossRef

35.

Netzer, O., Feldman, R., Goldenberg, J., & Fresko, M. (2012). Mine your own business: Market-structure surveillance through text mining. Marketing Science, 31(3), 521–543. https://doi.org/10.1287/mksc.1120.0713CrossRef

36.

Chen, K., Luo, P., & Wang, H. (2016). Investigating transitive influences on WOM: From the product network perspective. Electronic Commerce Research, 17(1), 149–167. https://doi.org/10.1007/s10660-016-9241-8CrossRef

37.

Tan, K. H., Ji, G., Lim, C. P., & Tseng, M.-L. (2017). Using big data to make better decisions in the digital economy. International Journal of Production Research, 55(17), 4998–5000. https://doi.org/10.1080/00207543.2017.1331051CrossRef

38.

Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. The Journal of Machine Learning Research, 3, 993–1022.

39.

Guo, Y., Barnes, S. J., & Jia, Q. (2017). Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation. Tourism Management, 59, 467–483. https://doi.org/10.1016/j.tourman.2016.09.009CrossRef

40.

Xiang, Z., Du, Q., Ma, Y., & Fan, W. (2017). A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism. Tourism Management, 58, 51–65. https://doi.org/10.1016/j.tourman.2016.10.001CrossRef

41.

Wang, Y., & Xu, W. (2018). Leveraging deep learning with LDA-based text analytics to detect automobile insurance fraud. Decision Support Systems, 105, 87–95. https://doi.org/10.1016/j.dss.2017.11.001CrossRef

42.

Duan, W., Cao, Q., Yu, Y., & Levy, S. (2013). Mining Online User-Generated Content: Using Sentiment Analysis Technique to Study Hotel Service Quality. Presented at the 46th Hawaii International Conference on System Sciences, Wailea, Maui, HI, USA.

43.

Lee, K., & Yu, C. (2018). Assessment of airport service quality: A complementary approach to measure perceived service quality based on Google reviews. Journal of Air Transport Management, 71, 28–44. https://doi.org/10.1016/j.jairtraman.2018.05.004CrossRef

44.

Bi, J.-W., Liu, Y., Fan, Z.-P., & Cambria, E. (2019). Modelling customer satisfaction from online reviews using ensemble neural network and effect-based Kano model. International Journal of Production Research, 57(22), 7068–7088. https://doi.org/10.1080/00207543.2019.1574989CrossRef

45.

Mirtalaie, M. A., Hussain, O. K., Chang, E., & Hussain, F. K. (2018). Extracting sentiment knowledge from pros/cons product reviews: Discovering features along with the polarity strength of their associated opinions. Expert Systems with Applications, 114, 267–288. https://doi.org/10.1016/j.eswa.2018.07.046CrossRef

46.

Eliacik, A. B., & Erdogan, N. (2018). Influential user weighted sentiment analysis on topic based microblogging community. Expert Systems with Applications, 92, 403–418. https://doi.org/10.1016/j.eswa.2017.10.006CrossRef

47.

Jeong, B., Yoon, J., & Lee, J.-M. (2017). Social media mining for product planning: A product opportunity mining approach based on topic modeling and sentiment analysis. International Journal of Information Management. https://doi.org/10.1016/j.ijinfomgt.2017.09.009CrossRef

48.

Jo, Y., & Oh, A. (2011). Aspect and sentiment unification model for online review analysis. Presented at the WSDM ’11 Proceedings of the fourth ACM international conference on Web search and data mining, HongKong, China.

49.

Qiu, J., Liu, C., Li, Y., & Lin, Z. (2018). Leveraging sentiment analysis at the aspects level to predict ratings of reviews. Information Sciences, 451–452, 295–309. https://doi.org/10.1016/j.ins.2018.04.009CrossRef

50.

Liu, B. (2015). Sentiment analysis: Mining opinions. Cambridge University Press.

51.

Le, Q. V., & Mikolov, T. (2014). Distributed Representations of Sentences and Documents. arXiv e-prints. Retrieved from http://arxiv.org/abs/1405.4053

52.

Elrod, T. (1991). Internal analysis of market structure: Recent developments and future prospects. Marketing Letters, 2(3), 253–266. https://doi.org/10.1007/BF00554130CrossRef

53.

Lee, T. Y., & BradLow, E. T. (2011). Automated marketing research using online customer reviews. Journal of Marketing Research (JMR), 48(5), 881–894. https://doi.org/10.1509/jmkr.48.5.881CrossRef

54.

Saaty, T. L. (2001). Analytic hierarchy process. In S. I. Gass & C. M. Harris (Eds.), Encyclopedia of Operations Research and Management Science (pp. 19–28). New York, NY: Springer US. https://doi.org/10.1007/1-4020-0611-X_31

55.

Saaty, T. L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48(1), 9–26. https://doi.org/10.1016/0377-2217(90)90057-ICrossRef

Titel: A novel approach for product competitive analysis based on online reviews
verfasst von: Zhen He
Lu Zheng
Shuguang He
Publikationsdatum: 14.02.2022
Verlag: Springer US
Erschienen in: Electronic Commerce Research / Ausgabe 4/2023
Print ISSN: 1389-5753
Elektronische ISSN: 1572-9362
DOI: https://doi.org/10.1007/s10660-022-09534-y

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