Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

A Natural Language Processing Approach to Mine Online Reviews Using Topic Modelling

  • Conference paper
  • First Online:
Computing Science, Communication and Security (COMS2 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1416))

Abstract

Artificial Intelligence is about how computers and humans communicate, and how we interact with language abbreviations. The ultimate purpose of the NLP is to connect in a way people can understand and reciprocate. Social networking messages have become a key source of consumer education. Sellers take online feedback to know if a potential buyer is a big part of their market. However, when such online reviews are too broad and/or extremely detailed, both buyers and sellers benefit from a mechanism that quickly extracts key insights from them. In this research paper, we used natural language processing to evaluate feedback from the language-processing community. Other data are included in the assessment of our peers.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Ma, B., Zhang, D., Yan, Z., Kim, T.: An LDA and synonym lexicon based approach to product feature extraction from online consumer product reviews. J. Electron. Commerc. Res. 14(4), 304 (2013)

    Google Scholar 

  2. Ghose, A., Ipeirotis, P.G., Li, B.: Designing ranking systems for hotels on travel search engines by mining user-generated and crowdsourced content. Mark. Sci. 31(3), 493–520 (2012)

    Article  Google Scholar 

  3. Chen, X., Faviez, C., Schuck, S., Lillo-Le-Louët, A., Texier, N., Dahamna, B., Huot, C., Foulquié, P., Pereira, S., Leroux, V.: Mining patients’ narratives in social media for pharma- covigilance: adverse effects and misuse of methylphenidate. Front. Pharmacol. 9, 541 (2018)

    Article  Google Scholar 

  4. Zhou, X., Wan, X., Xiao, J.: Representation learning for aspect category detection in online reviews. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)

    Google Scholar 

  5. Zhou, X., Tao, X., Rahman, M.M., Zhang, J.: Coupling topic modelling in opinion mining for social media analysis. In: Proceedings of the International Conference on Web Intelligence, pp. 533–540 (2017)

    Google Scholar 

  6. Chen, Z., Mukherjee, A., Liu, B.: Aspect extraction with automated prior knowledge learning. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 347–358 (2014)

    Google Scholar 

  7. Ibrahim, N.F., Wang, X.: Mining social network content of online retail brands: a machine learning approach. In: Proceedings of the 11th European Conference on Information Systems Management, ECISM, vol. 2011, pp. 129–138 (2017)

    Google Scholar 

  8. van Altena, A.J., Olabarriaga, S.D.: Predicting publication inclusion for diagnostic accuracy test reviews using random forests and topic modelling. In: CLEF (Working Notes) (2017)

    Google Scholar 

  9. Kar, A.K.: What affects usage satisfaction in mobile payments? Modelling user generated content to develop the “digital service usage satisfaction model. Inf. Syst. Front, pp. 1–21 (2020)

    Google Scholar 

  10. Schuckert, M., Liu, X., Law, R.: Hospitality and tourism online reviews: recent trends and future directions. J. Travel Tour. Mark. 32(5), 608–621 (2015)

    Article  Google Scholar 

  11. Suleman, K., Vechtomova, O.: Discovering aspects of online consumer reviews. J. Inf. Sci. 42(4), 492–506 (2016)

    Article  Google Scholar 

  12. Dessai, N.S.F., Laxminarayanan, J.A.: A topic modeling based approach for mining on- line social media data. In: 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), vol. 1, pp. 704–709 (2019)

    Google Scholar 

  13. Liu, B.: Opinion mining and sentiment analysis (2011)

    Google Scholar 

  14. Akhtar, N., Zubair, N., Kumar, A., Ahmad, T.: Aspect based sentiment oriented summarization of hotel reviews. Procedia Comput. Sci. 115, 563–571 (2017)

    Article  Google Scholar 

  15. Shatnawi, S., Gaber, M.M., Cocea, M.: Text stream mining for massive open online courses: review and perspectives. Syst. Sci. Control Eng. Open Access J. 2(1), 664–676 (2014)

    Article  Google Scholar 

  16. Kim, S., Zhang, J., Chen, Z., Oh, A.H., Liu, S.: A hierarchical aspect-sentiment model for online reviews. In: AAAI (2013)

    Google Scholar 

  17. Xiong, S., Wang, K., Ji, D., Wang, B.: A short text sentiment-topic model for product reviews. Neurocomputing 297, 94–102 (2018)

    Article  Google Scholar 

  18. Lu, B., Ott, M., Cardie, C., Tsou, B.K.: Multi-aspect sentiment analysis with topic models. In: 2011 IEEE 11th International Conference on Data Mining Workshops, pp. 81–88 (2011)

    Google Scholar 

  19. Puspaningrum, A., Siahaan, D., Fatichah, C.: Mobile app review labeling using lda similarity and term frequency-inverse cluster frequency (TF-ICF). In: 2018 10th International Conference on Information Technology and Electrical Engineering (ICITEE), pp. 365–370 (2018)

    Google Scholar 

  20. Mukherjee, A., Liu, B.: Aspect extraction through semi-supervised modeling. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 339–348 (2012)

    Google Scholar 

  21. Bahja, M., Lycett, M.: Identifying patient experience from online resources via sentiment analysis and topic modelling. In: Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, pp. 94–99 (2016)

    Google Scholar 

  22. Kee, Y.H., Li, C., Kong, L.C., Tang, C.J., Chuang, K.L.: Scoping review of mindfulness research: a topic modelling approach. Mindfulness, pp. 1–15 (2019)

    Google Scholar 

  23. Chehal, D., Gupta, P., Gulati, P.: Implementation and comparison of topic modeling techniques based on user reviews in e-commerce recommendations. J. Ambient Intell. Hum. Comput., pp. 1–16 (2020)

    Google Scholar 

  24. Chen, N., Lin, J., Hoi, S.C., Xiao, X., Zhang, B.: AR-miner: mining informative reviews for developers from mobile app marketplace. In: Proceedings of the 36th International Conference on Software Engineering, pp. 767–778 (2014)

    Google Scholar 

  25. Bi, J.W., Liu, Y., Fan, Z.P., Zhang, J.: Wisdom of crowds: conducting importance - performance analysis (IPA) through online reviews. Tour. Manage. 70, 460–478 (2019)

    Article  Google Scholar 

  26. Irawan, H., Akmalia, G., Masrury, R.A.: Mining tourist’s perception toward Indonesia tourism destination using sentiment analysis and topic modelling. In: Proceedings of the 2019 4th International Conference on Cloud Computing and Internet of Things, pp. 7–12 (2019)

    Google Scholar 

  27. Wang, W.: Sentiment analysis of online product reviews with semi-supervised topic sentiment mixture model. In: 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery. vol. 5, pp. 2385–2389 (2010)

    Google Scholar 

  28. Lucini, F.R., Tonetto, L.M., Fogliatto, F.S., Anzanello, M.J.: Text mining approach to explore dimensions of airline customer satisfaction using online customer reviews. J. Air Trans. Manage. 83, (2020)

    Article  Google Scholar 

  29. Moghaddam, S., Ester, M.: ILDA: interdependent LDA model for learning latent aspects and their ratings from online product reviews. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 665–674 (2011)

    Google Scholar 

  30. Brody, S., Elhadad, N.: An unsupervised aspect-sentiment model for online reviews. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 804–812 (2010)

    Google Scholar 

  31. Liu, B., Zhang, L.: A survey of opinion mining and sentiment analysis (2012)

    Google Scholar 

  32. Wang, W., Feng, Y., Dai, W.: Topic analysis of online reviews for two competitive products using latent Dirichlet allocation. Electron. Commerc. Res. Appl. 29, 142–156 (2018)

    Article  Google Scholar 

  33. Genc-Nayebi, N., Abran, A.: A systematic literature review: opinion mining studies from mobile app store user reviews. J. Syst. Softw. 125, 207–219 (2017)

    Article  Google Scholar 

  34. Zhao, X., Jiang, J., Yan, H., Li, X.: Jointly modeling aspects and opinions with a MaxEnt-LDA hybrid (2010)

    Google Scholar 

  35. Moro, S., Pires, G., Rita, P., Cortez, P.: A text mining and topic modelling perspective of ethnic marketing research. J. Bus. Res. 103, 275–285 (2019)

    Article  Google Scholar 

  36. Guerreiro, J., Rita, P.: How to predict explicit recommendations in online reviews using text mining and sentiment analysis. J. Hosp. Tour. Manage. 43, 269–272 (2020)

    Article  Google Scholar 

  37. Bansal, B., Srivastava, S.: Hybrid attribute based sentiment classification of online reviews for consumer intelligence. Appl. Intell. 49(1), 137–149 (2019)

    Article  Google Scholar 

  38. Bi, J.W., Liu, Y., Fan, Z.P., Cambria, E.: Modelling customer satisfaction from online reviews using ensemble neural network and effect-based Kano model. Int. J. Prod. Res. 57(22), 7068–7088 (2019)

    Article  Google Scholar 

  39. Xianghua, F., Guo, L., Yanyan, G., Zhiqiang, W.: Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon. Knowl. Based Syst. 37, 186–195 (2013)

    Article  Google Scholar 

  40. Jia, S.S.: Motivation and satisfaction of Chinese and US tourists in restaurants: a cross-cultural text mining of online reviews. Tour. Manage. 78, (2020)

    Article  Google Scholar 

  41. Bagheri, A., Saraee, M., Jong, F.D.: ADM-LDA: an aspect detection model based on topic modelling using the structure of review sentences. J. Inf. Sci. 40(5), 621–636 (2014)

    Article  Google Scholar 

  42. Titov, I., McDonald, R.: Modeling online reviews with multi-grain topic models. In: Proceedings of the 17th International Conference on World Wide Web, pp. 111–120 (2008)

    Google Scholar 

  43. Özdağoğlu, G., Kapucugil-Ikiz, A., Çelik, A.F.: Topic modelling-based decision framework for analysing digital voice of the customer. Total Qual. Manage. Bus. Excell. 29(13-14), 1545–1562 (2018)

    Google Scholar 

  44. Eickhoff, M., Neuss, N.: Topic modelling methodology: its use in information systems and other managerial disciplines (2017)

    Google Scholar 

  45. Guo, Y., Barnes, S.J., Jia, Q.: Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent Dirichlet allocation. Tour. Manage. 59, 467–483 (2017)

    Article  Google Scholar 

  46. Vanhala, M., Lu, C., Peltonen, J., Sundqvist, S., Nummenmaa, J., Järvelin, K.: The usage of large data sets in online consumer behaviour: a bibliometric and computational text-mining-driven analysis of previous research. J. Bus. Res. 106, 46–59 (2020)

    Article  Google Scholar 

  47. Kim, Y.B., Lee, J., Park, N., Choo, J., Kim, J.H., Kim, C.H.: When Bitcoin encounters information in an online forum: using text mining to analyse user opinions and predict value fluctuation. PLoS ONE 12(5), (2017)

    Article  Google Scholar 

  48. Nikolenko, S.I., Koltcov, S., Koltsova, O.: Topic modelling for qualitative studies. J. Inf. Sci. 43(1), 88–102 (2017)

    Article  Google Scholar 

  49. Archak, N., Ghose, A., Ipeirotis, P.G.: Deriving the pricing power of product features by mining consumer reviews. Manage. Sci. 57(8), 1485–1509 (2011)

    Article  Google Scholar 

  50. Dai, X., Spasic, I., Andres, F.: A framework for automated rating of online reviews against the underlying topics. In: Proceedings of the SouthEast Conference, pp. 164–167 (2017)

    Google Scholar 

  51. Blei, D.M.: Probabilistic topic models. Commun. ACM 55(4), 77–84 (2012)

    Article  Google Scholar 

  52. DiMaggio, P., Nag, M., Blei, D.: Exploiting affinities between topic modeling and the sociological perspective on culture: application to newspaper coverage of US government arts funding. Poetics 41(6), 570–606 (2013)

    Article  Google Scholar 

  53. Grimmer, J.: A Bayesian hierarchical topic model for political texts: measuring expressed agendas in Senate press releases. Pol. Anal. 18(1), 1–35 (2010)

    Article  Google Scholar 

  54. Jacobi, C., Atteveldt, W.V., Welbers, K.: Quantitative analysis of large amounts of journalistic texts using topic modelling. Digit. J. 4(1), 89–106 (2016)

    Google Scholar 

  55. Zhao, W., et al. A heuristic approach to determine an appropriate number of topics in topic modeling. In: BMC Bioinform. 16, 8 (2015)

    Google Scholar 

  56. Lancichinetti, A., Sirer, M.I., Wang, J.X., Acuna, D., Körding, K., Amaral, L.A.N.: High- reproducibility and high-accuracy method for automated topic classification. Phys. Rev. X 5(1), (2015)

    Google Scholar 

  57. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    Google Scholar 

  58. Alghamdi, R., Alfalqi, K.: A survey of topic modeling in text mining. Int. J. Adv. Comput. Sci. Appl.(IJACSA) 6(1) (2015)

    Google Scholar 

  59. Brocke, J.V., Mueller, O., Debortoli, S.: The power of text-mining in business process management (2016)

    Google Scholar 

  60. Mahmood, A.A.: Literature survey on topic modeling (2009)

    Google Scholar 

  61. Elgesem, D., Steskal, L., Diakopoulos, N.: Structure and content of the discourse on climate change in the blogosphere: the big picture. Environ. Commun. 9(2), 169–188 (2015)

    Article  Google Scholar 

  62. Koltsova, O., Koltcov, S.: Mapping the public agenda with topic modeling: the case of the Russian LiveJournal. Policy Internet 5(2), 207–227 (2013)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Universiti Teknologi Petronas, UTP, 32610, Seri Iskandar, Perak, Malaysia

    Usman Ahmad Usmani, Nazleeni Samiha Haron & Jaafreezal Jaafar

Authors
  1. Usman Ahmad Usmani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nazleeni Samiha Haron
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jaafreezal Jaafar
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Ganpat University, Gujarat, India

    Nirbhay Chaubey

  2. Ganpat University, Gujarat, India

    Satyen Parikh

  3. Ganpat University, Gujarat, India

    Kiran Amin

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Usmani, U.A., Haron, N.S., Jaafar, J. (2021). A Natural Language Processing Approach to Mine Online Reviews Using Topic Modelling. In: Chaubey, N., Parikh, S., Amin, K. (eds) Computing Science, Communication and Security. COMS2 2021. Communications in Computer and Information Science, vol 1416. Springer, Cham. https://doi.org/10.1007/978-3-030-76776-1_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-76776-1_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-76775-4

  • Online ISBN: 978-3-030-76776-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation