Title: An unsupervised service annotation by review analysis

Authors: Masafumi Yamamoto; Yuguan Xing; Toshihiko Yamasaki; Kiyoharu Aizawa

Addresses: Department of Information and Communication Engineering, The University of Tokyo, Tokyo, Japan ' Department of Information and Communication Engineering, The University of Tokyo, Tokyo, Japan ' Department of Information and Communication Engineering, The University of Tokyo, Tokyo, Japan ' Department of Information and Communication Engineering, The University of Tokyo, Tokyo, Japan

Abstract: With the increase in popularity of review sites, users can write reviews on services that they have used in addition to reading reviews by other users. However, a number of reviews make it almost impossible for users to read all the reviews in detail. It is even more burdensome to compare multiple services. Thus, useful tools for extracting the unique features of services are necessary so that users can easily and intuitively understand the quality of services and compare them. In this study, we present an unsupervised method for extracting the unique and detailed features of services and the users' opinions on these features. By using the term frequency and inverse document frequency (TF-IDF) algorithm, our method can also extract in particular the praised or criticised features of a specific service. We conducted evaluations to show the validity of our method. In addition, we implemented an intuitive graphical user interface.

Keywords: service annotation; service profiling; review analysis; summarisation.

DOI: 10.1504/IJBDI.2018.088288

International Journal of Big Data Intelligence, 2018 Vol.5 No.1/2, pp.73 - 89

Received: 04 May 2016
Accepted: 01 Dec 2016
Published online: 01 Dec 2017 *

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