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

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’.

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DOI
Digital Object Identifier link open in new tab 10.1007/978-3-319-67229-8_33
Category
Aktywność konferencyjna
Type
materiały konferencyjne indeksowane w Web of Science
Language
angielski
Publication year
2017

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