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Recently, people usually use the internet to obtain travel information, when they plan their travel. They especially want to obtain sightseeing spot information from reviews, but there are huge amounts of reviews of sightseeing spots. Users therefore cannot obtain important information from the reviews easily. As described herein, this paper aims to propose a system that automatically extracts and presents welcome news for sightseeing spots from reviews. This proposed Welcome-news is a “useful information” and “unexpected information” related to travel.

The flow for extracting Welcome-news from reviews is simple: A user inputs a sightseeing spot about which to get information; the system obtains reviews of the sightseeing spot and divides each sentence into reviews; the system extracts sentences including Welcome-news keyword(s), and the sentences become useful information; the system extracts unexpected information from useful information based on clustering, and it becomes Welcome-news; and the system presents all Welcome-news to the user.

This paper reports three findings: extraction of useful information for sightseeing spots based on Welcome-news keywords extracted by our experiment and using support vector machine (SVM); extraction of unexpected information for sightseeing spots by clustering; and automatic presentation of Welcome-news.

Numerous studies have extracted information from reviews based on some keywords. This proposed extraction of Welcome-news for travel not only uses keywords but also clusters based on topics. Furthermore, the proposed keywords include general keywords and unique keywords. The former appears for all kinds of sightseeing spots. The latter appears only for sightseeing spot. The authors extracted general keywords manually, and unique keywords using SVM.