Modeling the Customer’s Contextual Expectations Based on Latent Semantic Analysis Algorithms - Publication - Bridge of Knowledge

Search

Modeling the Customer’s Contextual Expectations Based on Latent Semantic Analysis Algorithms

Abstract

Nowadays, in the age of Internet, access to open data detects the huge possibilities for information retrieval. More and more often we hear about the concept of open data which is unrestricted access, in addition to reuse and analysis by external institutions, organizations and people. It’s such information that can be freely processed, add another data (so-called remix) and then published. More and more data are available in text format (such as reviews on books, movies, etc.). Algorithms of Latent Semantic Relations Analysis are one of the important tools for extraction and recognition of significant facts from textual data sets. Another aspect of research is to find ways and means of using the information obtained by Semantic tools applying in order to maximize the expected benefit. In this area, one of the modern tools for formulating the concept of benefits is the Benefits Language [1–5]. One of the conditions for the formation of the concept of benefits for studied product is the collection and processing © Springer International Publishing AG 2018 J. Świątek et al. (eds.), Information Systems Architecture and Technology: Proceedings of 38th International Conference on Information Systems Architecture and Technology – ISAT 2017, Advances in Intelligent Systems and Computing 656, DOI 10.1007/978-3-319-67229-8_33 of information about the client’s expectations. This process often requires additional time and financial costs. Therefore, one of the ways to obtain the “maximum benefit” of using the Benefits language is to develop a methodology of building the system of customer’s contextual expectations via systematic usage of benefit’s theory and algorithms of semantic analysis of textual opinions of open Internet pages’.

Citations

  • 1

    CrossRef

  • 0

    Web of Science

  • 4

    Scopus

Cite as

Full text

download paper
downloaded 41 times
Publication version
Accepted or Published Version
License
Copyright (Springer International Publishing AG 2018)

Keywords

Details

Category:
Conference activity
Type:
materiały konferencyjne indeksowane w Web of Science
Title of issue:
Information Systems Architecture and Technology: Proceedings of 38th International Conference on Information Systems Architecture and Technology – ISAT 2017. Part II strony 364 - 373
ISSN:
2194-5357
Language:
English
Publication year:
2017
Bibliographic description:
Rizun N., Ossowska K., Taranenko J..: Modeling the Customer’s Contextual Expectations Based on Latent Semantic Analysis Algorithms, W: Information Systems Architecture and Technology: Proceedings of 38th International Conference on Information Systems Architecture and Technology – ISAT 2017. Part II, 2017, Springer,.
DOI:
Digital Object Identifier (open in new tab) 10.1007/978-3-319-67229-8_33
Bibliography: test
  1. Ossowska K., Szewc L., Weichbroth P., Garnik I., Sikorski M.: Exploring ontological ap- proach for user requirements elicitation in design of online virtual agents// Information Sys- tems: Development, Research, Applications, Education/ ed. Stanisław Wrycza : Springer International Publishing, 2016, s.40-55 open in new tab
  2. Ossowska K., Szewc L., Orłowski C. The principles of model building concepts which are applied to the design patterns for Smart Cities (April. 2017), Intelligent Information and Database Systems. DOI: 10.1007/978-3-319-54430-4. open in new tab
  3. Ossowska K. Design modern technologies for older people by using expert systems contain- ing benefits language (May 2017), Zeszyty Naukowe Politechniki Poznańskiej, Organizacja i Zarządzanie open in new tab
  4. Ossowska, C.Orłowski, Projektowanie systemów informatycznych z wykorzystaniem języka korzyści (December 2016), Projektowanie i realizacja systemów informatycznych zarządzania. Wybrane aspekty
  5. Ossowska K., Czaja A, Model of personalization website using benefits language (Jun 2017)
  6. Baeza-Yates R., Ribeiro-Neto B. (2011) Modern Information Retrieval. Addison-Wesley, Wokingham, UK, 1999. Second edition
  7. Furnas G.W., Deerwester, S., Dumais S.T., Landauer T.K., Harshman R.A., Streeter L.A., Lochbaum K.E. (1998) Information retrieval using a singular value decomposition model of latent semantic structure. In Proc. ACM SIGIR Conf., s. 465-480, ACM, New York open in new tab
  8. Gerard Salton, Michael J. (1983) McGill Introduction to modern information retrieval. New York McGraw-Hill -McGraw-Hill computer science series, XV, 448 p
  9. Rizun N., Kapłanski P., Taranenko Y. (2016) Development and Research of the Text Mes- sages Semantic Clustering Methodology. 2016, Third European Network Intelligence Con- ference, Publisher: ENIC, # 33, pp.180-187 open in new tab
  10. Rizun N., Kapłanski P., Taranenko Y. (2016) Method of a Two-Level Text-Meaning Simi- larity Approximation of the Customers' Opinions. Economic Studies -Scientific Papers. University of Economics in Katowice, Nr. 296/2016, pp.64-85. open in new tab
  11. Rizun N., Taranenko Y. (2017) Development of the Algorithm of Polish Language Film Reviews Preprocessing. Proceeding of the 2nd International Conference on Information Technologies in Management, Publisher: Rocznik Naukowy Wydziału Zarządzania WSM, http://www.wsmciechanow.edu.pl/rocznik-naukowy/ (in print).
  12. Kapłanski P., Rizun N., Taranenko Y., Seganti A. (2016) Text-mining Similarity Approxi- mation Operators for Opinion Mining in BI tools. Chapter: Proceeding of the 11th Scientific Conference "Internet in the Information Society-2016", Publisher: University of Dąbrowa Górnicza, pp.121-141
  13. Salton G., Wong A., Yang C. S. (1975) A Vector Space Model for Automatic Indexing, Communications of the ACM, Vol. 18, Nr. 11, s. 613-620 open in new tab
  14. Dumais, S. T., Furnas, G. W., Landauer, T. K. and Deerwester, S. (1988) Using latent se- mantic analysis to improve information retrieval. In Proceedings of CHI'88: Conference on Human Factors in Computing, New York: ACM, 281-285 open in new tab
  15. Deerwester S., Susan T. Dumais, Harshman R. (1990) Indexing by Latent Semantic Analy- sis. http://lsa.colorado.edu/papers/JASIS.lsi.90.pdf open in new tab
  16. Eden L. (2007) Matrix Methods in Data Mining and Pattern Recognition, SIAM. open in new tab
  17. Bahl L., Baker J., Jelinek E., & Mercer R. (1977) Perplexity -a measure of the difficulty of speech recognition tasks. In Program, 94th Meeting of the Acoustical Society of America, volume 62, page S63.
  18. Blei D., Ng A., Jordan M. (2003) Latent Dirichlet allocation. Journal of Machine Learning Research, 3: pp. 993-1022. open in new tab
  19. Blei, David M. (2012) Introduction to Probabilistic Topic Models. Comm. ACM 55 (4), April, 2012: pp. 77-84 open in new tab
  20. Daud Ali, Li Juanzi, Zhou Lizhu, Muhammad Faqir (2010) Knowledge discovery through directed probabilistic topic models: a survey. In Proceedings of Frontiers of Computer Sci- ence in China. pp. 280-301.
  21. David M. Blei. Topic modeling. http: //www.cs.princeton.edu/~blei/topicmodeling.html open in new tab
Verified by:
Gdańsk University of Technology

seen 111 times

Recommended for you

Meta Tags