Skip to main content
SpringerLink
Log in

New information search model for online reviews with the perspective of user requirements

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Many e-commerce websites currently provide online reviews to share e-shoppers’ experience with the products. To help e-shoppers obtaining information efficiently, these websites usually summarize product information based on their certain predefined aspects. However, e-shopper’s aspects should be annotated to make sure that more highly related information of online reviews can be fetched for fulfilling e-shopper’s requirements. Hence, this study integrates an annotation approach with similarity techniques (Keyword pair similarity and Aspect-sentence similarity) to propose a new framework to fetch more highly correlated sentences for e-shoppers. Experimental results show that most of the combinations in the proposed approach have high prediction performance in the Top 10 sentences with Precision (0.90 or higher).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Aroyo L, De Meo, P, Musial K, Rosaci D, Sarnè GML (2017) Using Centrality Measures to Predict Helpfulness-based Reputation in Trust Networks. ACM Transactions on Internet Technology (ACM TOIT) 17(1):1–20

    Article  Google Scholar 

  2. Bellini P, Palesi LAI, Nesi P, Pantaleo G (2022) Multi clustering recommendation system for fashion retail. Multimed Tools Appl: 1–28. https://doi.org/10.1007/s11042-021-11837-5

  3. Chen PI, Lin SJ (2010) Automatic keyword prediction using Google similarity distance. Expert Syst Appl 37(3):1928–1938

    Article  Google Scholar 

  4. Chen PI, Lin SJ (2011) Word AdHoc network: using Google core distance to extract the most relevant information. Knowl-Based Syst 24(3):393–405

    Article  Google Scholar 

  5. Chen PI, Lin SJ, Chu YC (2011) Using Google latent semantic distance to extract the most relevant information. Expert Syst Appl 38(6):7349–7358

    Article  Google Scholar 

  6. Chen RC, Bau CT, Yeh CJ (2011) Merging domain ontologies based on the WordNet system and fuzzy formal concept analysis techniques. Appl Soft Comput 11(2):1908–1923

    Article  Google Scholar 

  7. Chen L, Qi L, Wang F (2012) Comparison of feature-level learning methods for mining online consumer reviews. Expert Syst Appl 39(10):9588–9601

    Article  Google Scholar 

  8. Cilibrasi RL, Vitanyi PM (2007) The google similarity distance. IEEE Trans Knowl Data Eng 19(3):370–383

    Article  Google Scholar 

  9. Cruz FL, Troyano JA, Enríquez F, Ortega FJ, Vallejo CG (2013) ‘Long autonomy or long delay?‘the importance of domain in opinion mining. Expert Syst Appl 40(8):3174–3184

    Article  Google Scholar 

  10. Dave K, Lawrence S, Pennock DM (2003) Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In: Proceedings of the 12th international conference on World Wide Web. ACM, pp 519–528

  11. DeLone WH, McLean ER (1992) Information systems success: the quest for the dependent variable. Inf Syst Res 3(1):60–65

    Article  Google Scholar 

  12. Eirinaki M, Pisal S, Singh J (2012) Feature-based opinion mining and ranking. J Comput Syst Sci 78(4):1175–1184

    Article  MathSciNet  Google Scholar 

  13. Fotia L, Messina F, Rosaci D, Sarnè GML (2017) Using Local Trust for Forming Cohesive Social Structures in Virtual Communities. Comput J 60(11):1717–1727. Oxford University Press

  14. Furner, CP, Zinko, RA (2016) The influence of information overload on the development of trust and purchase intention based on online product reviews in a mobile vs. web environment: an empirical investigation. Electron Markets, https://doi.org/10.1007/s12525-016-0233-2

  15. Gao JB, Zhang BW, Chen XH (2015) A WordNet-based semantic similarity measurement combining edge-counting and information content theory. Eng Appl Artif Intell 39:80–88

    Article  Google Scholar 

  16. Gharib TF, Fouad MM, Aref MM (2009) Fuzzy document clustering approach using WordNet lexical categories. In advanced techniques in computing sciences and software engineering. Springer Netherlands, Dordrecht, pp 181–186

  17. Gligorov R, Ten Kate W, Aleksovski Z, Van Harmelen F (2007) Using Google distance to weight approximate ontology matches. In: Proceedings of the 16th international conference on World Wide Web. ACM, pp 767–776

  18. Han EH, Karypis G (2005) Feature-based recommendation system. In: Proceedings of the 14th ACM international conference on Information and knowledge management. ACM, pp 446–452

  19. Haruna K, Akmar Ismail M, Suhendroyono S, Damiasih D, Pierewan AC, Chiroma H, Herawan T (2017) Context-aware recommender system: a review of recent developmental process and future research direction. Appl Sci 7(12):1211

    Article  Google Scholar 

  20. Hu M, Liu B (2004) Mining opinion features in customer reviews. In AAAI 4(4):755–760

    Google Scholar 

  21. Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 168–177

  22. Huang TCK, Chen YL, Chen MC (2016) A novel recommendation model with Google similarity. Decis Support Syst 89:17–27

    Article  Google Scholar 

  23. Jiang Y, Wang X, Zheng HT (2014) A semantic similarity measure based on information distance for ontology alignment. Inf Sci 278:76–87

    Article  Google Scholar 

  24. Kansal H, Toshniwal D (2014) Aspect based summarization of context dependent opinion words. Procedia Computer Science 35:166–175

    Article  Google Scholar 

  25. Korde V, Mahender CN (2012) Text classification and classifiers: a survey. International Journal of Intelligence & Applications 3(2):85–99

    Article  Google Scholar 

  26. Lai CY, Li YM, Lin LF (2017) A social referral appraising mechanism for the e-marketplace. Inf Manag 54(3):269–280

    Article  Google Scholar 

  27. Li M, Chen X, Li X, Ma B, Vitányi PM (2004) The similarity metric. Information Theory, IEEE Transactions on 50(12):3250–3264

    Article  MATH  MathSciNet  Google Scholar 

  28. Li CH, Yang JC, Park SC (2012) Text categorization algorithms using semantic approaches, corpus-based thesaurus and WordNet. Expert Syst Appl 39(1):765–772

    Article  Google Scholar 

  29. Lin KP, Shen CY, Chang TL, Chang TM (2017) A consumer review-driven recommender service for web e-commerce. In: 2017 IEEE 10th Conference on Service-Oriented Computing and Applications (SOCA). IEEE, pp 206–210

  30. Liu H, Bao H, Xu D (2012) Concept vector for semantic similarity and relatedness based on WordNet structure. J Syst Softw 85(2):370–381

    Article  Google Scholar 

  31. Liu H, He J, Wang T, Song W, Du X (2013) Combining user preferences and user opinions for accurate recommendation. Electron Commer Res Appl 12(1):14–23

    Article  Google Scholar 

  32. Lu X, Ba S, Huang L, Feng Y (2013) Promotional marketing or word-of-mouth? Evidence from online restaurant reviews. Inf Syst Res 24(3):596–612

    Article  Google Scholar 

  33. Makrehchi M, Kamel MS (2007) Automatic taxonomy extraction using google and term dependency. In: IEEE/WIC/ACM International Conference on Web Intelligence (WI'07). IEEE, pp 321–325

  34. Schütze H, Manning CD, Raghavan P (2008) Introduction to information retrieval. Cambridge University Press, Cambridge, vol 39, pp 234–265

  35. Marrese-Taylor E, Velásquez JD, Bravo-Marquez F (2014) A novel deterministic approach for aspect-based opinion mining in tourism products reviews. Expert Syst Appl 41(17):7764–7775

    Article  Google Scholar 

  36. Miller GA (1995) WordNet: a lexical database for English. Commun ACM 38(11):39–41

    Article  Google Scholar 

  37. Moghaddam S, Jamali M, Ester M (2011) Review recommendation: personalized prediction of the quality of online reviews. In: Proceedings of the 20th ACM international conference on Information and knowledge management, pp 2249–2252

  38. Chelliah M, Sarkar S (2017) Product recommendations enhanced with reviews. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, pp 398–399

  39. O’Mahony MP, Cunningham P, Smyth B (2009) An assessment of machine learning techniques for review recommendation. In Irish conference on artificial intelligence and cognitive science (pp. 241-250). Springer, Berlin, Heidelberg

    Google Scholar 

  40. Oliver RL (1977) Effect of expectation of disconfirmation on postexposure product evaluations – an alternative interpretation. J Appl Psych 64(4):480

    Article  Google Scholar 

  41. Palopoli L, Rosaci D, Sarné GM (2016) A distributed and multi-tiered software architecture for assessing e-commerce recommendations. Concurrency and Computation: Practice and Experience 28(18):4507–4531

    Article  Google Scholar 

  42. Paul D, Sarkar S, Chelliah M, Kalyan, C, Sinai Nadkarni PP (2017) Recommendation of high quality representative reviews in e-commerce. In: Proceedings of the eleventh ACM conference on recommender systems, pp 311–315

  43. Pedersen T, Patwardhan S, Michelizzi J (2004) WordNet:: Similarity: measuring the relatedness of concepts. In: AAAI, vol 4, pp 25–29

  44. Peñalver-Martinez I, Garcia-Sanchez F, Valencia-Garcia R, Rodríguez-García MÁ, Moreno V, Fraga A, Sánchez-Cervantes JL (2014) Feature-based opinion mining through ontologies. Expert Syst Appl 41(13):5995–6008

    Article  Google Scholar 

  45. Popescu AM, Etzioni O (2007) Extracting product features and opinions from reviews. In natural language processing and text mining (pp. 9-28). Springer, London

    Google Scholar 

  46. Qiu G, Liu B, Bu J, Chen C (2011) Opinion word expansion and target extraction through double propagation. Computational linguistics 37(1):9–27

    Article  Google Scholar 

  47. Salehan M, Kim DJ (2016) Predicting the performance of online consumer reviews: a sentiment mining approach to big data analytics. Decis Support Syst 81:30–40

    Article  Google Scholar 

  48. Salton G (1983) Introduction to modern information retrieval. McGraw-Hill, New York

  49. Tewari AS, Jain R, Singh JP, Barman AG (2019) Personalized product recommendation using aspect-based opinion mining of reviews. In proceedings of international ethical hacking conference 2018 (pp. 443–453). Springer, Singapore

    Google Scholar 

  50. Wang H, Lu Y, Zhai C (2011) Latent aspect rating analysis without aspect keyword supervision. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 618–626

  51. Wang W, Xu H, Wan W (2013) Implicit feature identification via hybrid association rule mining. Expert Syst Appl 40(9):3518–3531

    Article  Google Scholar 

  52. Wang JZ, Yan Z, Yang LT, Huang BX (2015) An approach to rank reviews by fusing and mining opinions based on review pertinence. Information Fusion 23:3–15

    Article  Google Scholar 

  53. Weichselbraun A, Gindl S, Scharl A (2014) Enriching semantic knowledge bases for opinion mining in big data applications. Knowl-Based Syst 69:78–85

    Article  Google Scholar 

  54. Willett P (2006) The porter stemming algorithm: then and now. Program 40(3):219–223

    Article  Google Scholar 

  55. You W, Xia M, Liu L, Liu D (2012) Customer knowledge discovery from online reviews. Electron Mark 22(3):131–142

    Article  Google Scholar 

  56. Zhang L, Liu B (2011) Identifying noun product features that imply opinions. In: Proceedings of the 49th annual meeting of the Association for Computational Linguistics: human language technologies, pp 575–580

Download references

Author information

Authors and Affiliations

  1. Department of Information Management, National Chin-Yi University of Technology, Taichung, Taiwan

    Cheng-Hsiung Weng

  2. Department of Business Administration, National Chung Cheng University, 168, University Rd., Min-Hsiung, Chia-Yi, Taiwan

    Cheng-Kui Huang

  3. Department of Information Management, National Central University, Chung-Li, Taoyuan City, Taiwan

    Yen-Liang Chen & Yu-Shan Huang

Authors
  1. Cheng-Hsiung Weng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cheng-Kui Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yen-Liang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yu-Shan Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cheng-Kui Huang.

Ethics declarations

Conflict of interest

There is no conflict of interest in this study.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Weng, CH., Huang, CK., Chen, YL. et al. New information search model for online reviews with the perspective of user requirements. Multimed Tools Appl 82, 28165–28185 (2023). https://doi.org/10.1007/s11042-023-14847-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-14847-7

Keywords

Search

Navigation