Skip to main content
Log in

A Novel Mapper Machine Learning Algorithm for Semantic Domain Mapping for Domain Database Updation

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

The opinions and reviews analysis has been an important part of research as people around the globe are using these data for making decision for the purchase of services or product. The analysis and summarization of this has also benefited the service provider and manufacturer for the updation. The analysis methodology requires the updated domain information for mapping the opinions and reviews to the particular domain entity; if it is not updated, the analysis will not be accurate as these opinions that belong to the domain are left unmatched, as they were not updated in the domain information. This paper deals with the design and development of Novel Mapper algorithm that will be used for the predication of the particular entity and will add it to the particular domain information. The proposed novel mapper algorithm uses the Levenshtein Distance for calculating the distance between the matched and unmatched entity. The proposed methodology is compared with different existing algorithms for calculating the accuracy and efficiency of the proposed model.

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

Access this article

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

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data Availability

Two different datasets are used in this research: Tripadvisor, Available online: http://www.tripadvisor.com (accessed on 1 November 2021), Zenodo, Available online: https://zenodo.org/record/1219899#.YWaztBpBxPY (accessed on 1 November 2021)

References

  1. Jacobs PS (1994) Text-based systems and information management: artificial intelligence confronts matters of scale. In: Jacobs, P.S Proceedings sixth international conference on tools with artificial intelligence. Doi: https://doi.org/10.1109/TAI.1994.346487.

  2. Liu C-L, Jaeger S, Nakagawa M. Online recognition of chinese characters: the State-of-the-Art. IEEE Trans Pattern Anal Mach Intell. 2004;26:198–213. https://doi.org/10.1109/TPAMI.2004.1262182.

    Article  Google Scholar 

  3. Soo-Min Kim, Eduard Hovy. 2006. Automatic Identification of Pro and Con Reasons in Online Reviews. In Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions, pages 483–490, Sydney, Australia. Association for Computational Linguistics.

  4. Wang, Hongning & Lu, Yue & Zhai, Chengxiang. (2010). Latent Aspect Rating Analysis on Review Text Data: A Rating Regression Approach. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 783–792. https://doi.org/10.1145/1835804.1835903.

  5. Ghanbari A, Hadavandi E, Abbasian-Naghneh S. Comparison of Artificial intelligence based techniques for short term load forecasting. Fifth Int Conf Bus Intel Financ Eng. 2010;2012:6–10. https://doi.org/10.1109/BIFE.2010.12.

    Article  MATH  Google Scholar 

  6. Zhu J, Zhang C, Ma MY. Multi-aspect rating inference with aspect-based segmentation in IEEE transactions on affective computing. Fourth Quarter. 2012;3:469–81. https://doi.org/10.1109/T-AFFC.2012.18.

    Article  Google Scholar 

  7. Liu C-L, Hsaio W-H, Lee C-H, Lu G-C, Jou E. Movie rating and review summarization in mobile environment in IEEE transactions on systems, man, and cybernetics. Part C Appl Rev. 2012;42(3):397–407. https://doi.org/10.1109/TSMCC.2011.2136334.

    Article  Google Scholar 

  8. Xueke Xu, Xueqi C, Songbo T, Yue L, Shen H. Aspect-level opinion mining of online customer reviews. Commun China. 2013;10:25–41. https://doi.org/10.1109/CC.2013.6488828.

    Article  Google Scholar 

  9. McAuley, Julian and Leskovec, (2013). Hidden factors and hidden topics: Understanding rating dimensions with review text. In: 2013—Proceedings of the 7th ACM Conference on Recommender Systems. p. 165–172. https://doi.org/10.1145/2507157.2507163.

  10. Grimmer J, Stewart B. Text as data: the promise and pitfalls of automatic content analysis methods for political texts. Polit Anal. 2013;21:267–97. https://doi.org/10.1093/pan/mps028.

    Article  Google Scholar 

  11. Zhang M-L, Zhou Z-H. A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng. 2014;26(8):1819–37. https://doi.org/10.1109/TKDE.2013.39.

    Article  Google Scholar 

  12. Wu DD, Zheng L, Olson DL. A decision support approach for online stock forum sentiment analysis. IEEE Transact Syst Man Cyber: Syst. 2014;44(8):1077–87. https://doi.org/10.1109/TSMC.2013.2295353.

    Article  Google Scholar 

  13. Susan M, David S, Ermira Z "What’s “Funny” about Technology Adoption? Humorous Appropriation of Online Review Platforms" (2016). ICIS 2016 Proceedings. 10.https://aisel.aisnet.org/icis2016/ITImplementation/Presentations/10

  14. Kansal H, Toshniwal D. Aspect based summarization of context dependent opinion words. Proce Comp Sci. 2014;35:166–75. https://doi.org/10.1016/j.procs.2014.08.096.

    Article  Google Scholar 

  15. Zhang X, Cui L, Wang Y. commtrust: computing multi-dimensional trust by mining e-commerce feedback comments. IEEE Trans Knowl Data Eng. 2014;26(7):1631–43. https://doi.org/10.1109/TKDE.2013.177.

    Article  Google Scholar 

  16. Ordenes FV, Theodoulidis B, Burton J, Gruber T, Zaki M. Analyzing customer experience feedback using text mining: a linguistics-based approach. J Serv Res. 2014;17(3):278–95. https://doi.org/10.1177/1094670514524625.

    Article  Google Scholar 

  17. Liu S, Cheng X, Li F, Li F. TASC: topic-adaptive sentiment classification on dynamic tweets. IEEE Trans Knowled Data Eng. 2015;27:1696–709. https://doi.org/10.1109/TKDE.2014.2382600.

    Article  Google Scholar 

  18. Johnson A, Ghassemi M, Nemati S, Niehaus K, Clifton D, Clifford G. Machine learning and decision support in critical care. Proc IEEE. 2016;104:444–66. https://doi.org/10.1109/JPROC.2015.2501978.

    Article  Google Scholar 

  19. Sun, Xiao & Sun, Chongyuan & Ren, Fuji & Tian, Fang & Wang, Kunxia. Emotional element detection and tendency judgment based on mixed model with deep features. 2016; https://doi.org/10.1109/ICIS.2016.7550779.

  20. Agrawal P, Agrawal A. Opinion analysis using domain ontology for implementing natural language based feedback system. Int J Infor Technol Comp Sci. 2014;6:61–9. https://doi.org/10.5815/ijitcs.2014.03.08.

    Article  Google Scholar 

Download references

Funding

No funding from any organization was involved.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Pratik K. Agrawal.

Ethics declarations

Conflict of Interest

Pratik Agrawal declares that he has no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

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

This article is part of the topical collection “Machine Intelligence and Smart Systems” guest edited by Manish Gupta and Shikha Agrawal.

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

Agrawal, P.K. A Novel Mapper Machine Learning Algorithm for Semantic Domain Mapping for Domain Database Updation. SN COMPUT. SCI. 4, 536 (2023). https://doi.org/10.1007/s42979-023-02036-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-023-02036-0

Keywords

Navigation