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ThinkMind // MMEDIA 2019, The Eleventh International Conference on Advances in Multimedia // View article mmedia_2019_1_20_58003


Identifying Obscure Venues Using Classification of User Reviews

Authors:
Masaharu Hirota
Masaki Endo
Hiroshi Ishikawa

Keywords: Tourism information; Text classification; Support Vector Machine;Review Analysis

Abstract:
Today, tourism occupies an essential position in many countries as a critical industry. When sightseeing, many people visit different places such as restaurants, hotels, and tourist spots. Some of these venues, while worthwhile, are considered obscure, secret, not well-known, or having little popularity. Their extraction and recommendation are vital to improving the satisfaction of tourists. Although some studies have been proposed on extracting obscure venues based on their degree of popularity, the interest in such venues varies from person to person. In addition, these studies have defined what constitutes an obscure venue and use such criteria for venue extraction. This study proposes a method for discovering obscure venues using classifiers for identifying reviews, including obscure impressions. To achieve this goal, in this study, a model was developed to classify venues as obscure or not obscure using reviews with language indicating their obscurity. This study also analyzes the differences among venues perceived by reviewers as being obscure. We demonstrate the performance of the proposed approach by indicating that the posting destination of obscure reviews differs for each user.

Pages: 7 to 12

Copyright: Copyright (c) IARIA, 2019

Publication date: March 24, 2019

Published in: conference

ISSN: 2308-4448

ISBN: 978-1-61208-697-2

Location: Valencia, Spain

Dates: from March 24, 2019 to March 28, 2019

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