Automatic Compilation of an Online Travel Portal from Automatically Extracted Travel Blog Entries

There is a huge demand on multilingual tourism information of Japan because of the increasing number of tourists from foreign countries. Most of them may expect typical and stereotyped culture, nature, and modern society of Japan. However, people from different backgrounds, cultures, and languages might expect different aspects of Japan, as well. In this paper, we analyze these kinds of differences as the cultural tourism preference for Japan. We propose a machine-learning-based method to figure out the cultural tourism preference of people of different countries based on comparing the access logs to a multilingual tourism information site in different languages. We focus our discussion on the pages accessed in Thai and Vietnamese languages. Our research result shows that for Thai tourists the characteristic features are the famous places in an area and local specialties, but Vietnamese tourists pay much more attention to facilities and location of hotels. This difference was not observable by naive extraction of keywords and their visualization. This result has been used as a guide to the further creation of content in the tourism information site.

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“…Ishino et al . compared tourism‐related blogs with general blog articles and obtained feature words that designate tourism blogs. Okumura et al .…”

Section: Related Workmentioning

confidence: 99%

Text mining of tourism preference in a multilingual site

Zeng

Nakatoh

Hirokawa

et al. 2018

IEEJ Transactions Elec Engng

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show abstract

“…From travel blog entries, which were automatically identified using the method mentioned in Section 2.1, Ishino et al [15] extracted the hyperlinks to useful Web sites for a tourist spot included by bloggers, and thereby constructed collections of hyperlinks for tourist spots. The procedure for classifying links in travel blog entries is as follows.…”

Section: Automatic Compilation Of Travel Linksmentioning

confidence: 99%

“…Ishino et al [15] proposed a method to extract people's transportation information from automatically identified travel blogs written in Japanese [25]. They used machine learning to extract information, such as "departure place", "destination", or "transportation device", from travel blog entries.…”

Section: Travelers' Behavior Analysismentioning

confidence: 99%

“…They used the same cues to classify travel information links [15] (see Section 2.3), and then extracted keywords from the citing areas of links of types S (Spot) and R (Restaurant). 10 For calculating the similarity between two photos, they used the Bag-of-Visual-Words representation [18,26], which represents an image as a set of salient regions (visual words), called Bag-of-Visual-Words vectors.…”

Section: Recommending Travel Productsmentioning

confidence: 99%

“…15 Hiroden blog entries were defined as travel journals that provide regional information for streetcar stations in Hiroshima. The task of collecting Hiroden blog entries was divided into two steps: (1) collection of blog entries; and (2) identification of Hiroden blog entries.…”

Section: Providing Travel Information Along Streetcar Linesmentioning

confidence: 99%

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