Construction of a System for Providing Travel Information
along Hiroden Streetcar Lines

Aya Ishino

Hidetsugu Nanba

Toshiyuki Takezawa

Graduate School of Information Sciences, Hiroshima City University
Hiroshima, Japan{ishino, nanba, takezawa}@ls.info.hiroshima-cu.ac.jp

Abstract— We propose a method for collecting Hiroshima
Electric Railway (Hiroden) blog entries in a blog database.
Hiroden blog entries are defined as travel journals thatprovide regional information at Hiroden streetcar stations. The
task of collecting Hiroden blog entries is divided into two steps:(1) collection of blog entries, and (2) identification of Hiroden
blog entries. To investigate the effectiveness of our method, we
conducted some experiments. From the experimental results,
we obtained precision of 82.4%, recall of 64.5%, and Fmeasure of 72.4% in automatic identification of Hiroden blog
entries. Finally, we have constructed a system for providing
travel information along Hiroden streetcar lines.

methods. To investigate the effectiveness of our methods, we
conducted some experiments, and Section V reports on theseand the results. We present some conclusions in Section VI.
II.

SYSTEM BEHAVIOR

In this section, we describe our prototype system. Fig. 1
is a snapshot of its behavior on the iPad. The system operates
with a browser.

Keywords-component; blog; information extraction; travel
informationI.

INTRODUCTION

In Japan, the Basic Act for Promoting a Tourism-oriented
Country became effective in January 2007. The Japan
Tourism Agency was launched in October 2008, and aims topromote domestic and overseas tourism. At the moment,
various events relating to NHK’s period drama “Taira no
Kiyomori” are being held in Hiroshima, Japan. From April
2013, the biggest confectionery exposition in Japan isscheduled to be held in Hiroshima (Hiroshima Kashihaku
2013). These are examples of the numerous efforts toincrease the number of travelers who visit Hiroshima.
Our focus is on the Hiroshima Electric Railway, known
as “Hiroden” for short. Its routes in Hiroshima are tourist
resources. In this paper, we propose a method for collecting
Hiroden blog entries in a blog database. Hiroden blog entries
are defined as travel journals that provide information along
Hiroden streetcar stations. In addition, we construct a systemfor providing Hiroden blog entries as travel information at
Hiroden streetcar lines. We consider that the system canpromote tourism using Hiroden and provide regional
information not found in guidebooks.
Recently, several methods to identify bloggers’ attributes
such as residential area [1], gender, and age [2, 3], have been
proposed. By combining this research with Hiroden blog
entries, it is possible to recommend a local product that is
popular among females, for example, or a travel spot, thatyoung people often visit.
The remainder of this paper is organized as follows.
Section II shows the system behavior in terms of snapshots.
Section III describes related work. Section IV explains our

Figure 1. A snapshot from our system on the iPad.

Fig. 2 shows a route map of the system for providing
travel information along Hiroden streetcar lines. The route
map contains Hiroden streetcar stations and major tourist
spots. These are the steps in the search procedure.
(Step 1) Click a part of the route map, such as “Kamiya-choarea” (shows as ① in Fig. 2), to show an enlarged routemap (Fig. 3).
(Step 2) Click the Hiroden streetcar station, such as “Atomic
Bomb Dome” (shown as ② in Fig. 3), to generate a listof links to Hiroden blog entries (Fig. 4).
(Step 3) Click the link to a Hiroden blog entry to display it.
In this paper, we propose a method for collecting the
Hiroden blog entries that the system provides.

III.
①

Figure 2. The opening page of our travel information system: a route mapof the Hiroden system.

②

Figure 3. A portion of the route map, enlarged.

In this section, we describe some related studies.
GeoCLEF (http://ir.shef.ac.uk/geoclef/) is the cross-language
geographic retrieval track run as part of the Cross Language
Evaluation Forum (CLEF http://www.clef-initiative.eu/), andit has been operating since 2005 [4]. The goal of this task is
to retrieve news articles relevant to particular aspects ofgeographic information, such as “wine regions around therivers in Europe.” In our work, we focus on Hiroden blog
entries rather than news articles, because bloggers’ travel
experiences tend to be written as blog entries.
In this paper, we propose a method for collecting
Hiroden blog entries as regional information. Some otherstudies have also proposed methods for collecting regional
information from the Web. Otsuki and Sato [5] proposed asystem that edits a Web directory of regional information
automatically. They focused on regional Web sites provided
by local governments and classified the pages in the regional
web sites into eight categories. Okamoto et al. [6] proposed amethod for extracting occasional or volatile local eventsfrom blogs using search queries. In this paper, we focus on
blog entries.
A Hiroden blog entry is a travel blog entry. Both“www.travelblog.org” and “travel.blogmura.com” are portalsites for travel blogs. At these sites, travel blogs are manuallyregistered by bloggers themselves, and the blog entries areclassified by their destinations. However, there are many
more Hiroden blog entries in the blogosphere. There are few
travel blogs about a confined geographical area like Hiroden.
Aiming to construct an exhaustive database of Hiroden blog
entries, we have studied their automatic identification in the
blogosphere. Nanba et al. [7] identified travel blog entries ina blog database. They formulated the identification of travel
blog entries as a sequence-labeling problem, and solved itusing machine learning. They extracted pairs comprising alocation name and a local product from these entries andshowed the effectiveness of travel blog entries as a source oftravel information. In this paper, we propose a method for
identifying Hiroden blog entries in a blog database.
There are some studies of extracting of transportation
information. Davidov [8] presented an algorithm frameworkthat enabled automated acquisition of map-link informationfrom the Web, based on surface patterns such as “from X toY.” Given a set of locations as initial seeds, they retrieved an
extended set of locations from the Web and produced a maplink network that connects these locations using transporttype edges. Ishino et al. [9] extracted transportation
information automatically from travel blog entries via
machine-learning techniques. By combining this researchwith our method, it is possible to recommend an optimum
route using Hiroden.
IV.

Figure 4. A list of links to Hiroden blog entries.

RELATED WORKCOLLECTION OF HIRODEN BLOG ENTRIESIn this section, we describe our method for collecting
Hiroden blog entries. This task is divided into two steps: (1)collection of blog entries; and (2) identification of Hiroden
blog entries. These steps are explained below.

A. Collection of Blog Entries
The blog entries to be collected should include
information about the 78 Hiroden streetcar stations. We
therefore searched blogs using Yahoo! search (blog) for each
Hiroden streetcar station. As a result, we collected 1,748
blog entries.
B. Identification of Hiroden Blog Entries
In this paper, we define a Hiroden blog entry as one inwhich the author alighted at a Hiroden streetcar station andsaw sights around the station. The blog entries collected in
Section IV-A contain non-Hiroden blog entries, for example,
blogs about other means of transportation or blogs whose
authors did not see sights. Then, we propose a method for
identifying Hiroden blog entries among the blog entries
collected in Section IV-A.
Fig. 5 shows an example of a Hiroden blog entry. It
contains the names of Hiroden streetcar stations, words
related to Hiroden such as “広電 ” (Hiroden) or “市電”
(streetcar in Hiroshima city), and words related tosightseeing such as “観光” (sightseeing) or “散策” (stroll).
In addition, when blog authors see a sight, they tend to takepictures there and post them to their blog entries. Therefore,
Hiroden blog entries contain pictures that authors took in
seeing sights around a Hiroden streetcar station. We
employed a machine-learning technique to identify Hiroden
blog entries. We used the following features for machinelearning. The feature words were selected manually.
 Whether the word is a name of a Hiroden streetcar
station (78).
 Whether the word is related to Hiroden, such as “広電” (Hiroden) or “市電” (streetcar) (5).
 Whether the word related to other means oftransportation, such as “新幹線” (Sinkansen bullet
train) or “フェリー” (ferry) (6).
 Whether the word related to sightseeing, such as “観光” (sightseeing) or “散策” (stroll) (9).
 The number of pictures.
[original]
ココに車を停めて後の移動は市内電車広電
市電と宮島行の船が一日中乗り放題の一日乗車乗船券を買ってまずは原爆ドーム前へ
周辺のテキヤを満喫し広電で宮島口まで移動[translation]
I parked here. After this, I used Hiroden for transportation.
I got a 1-Day Trip Card for enjoying streetcars and a ferryboat to Miyajima
and went to Atomic Bomb Dome Station.
I visited the adjacent stands.
Next, I went to Hiroden-miyajima-gichi Station by Hiroden.
Figure 5. An example of a Hiroden blog entry.

V.

EXPERIMENTS

To investigate the effectiveness of our methods, we
conducted several experiments.

A. Data Sets and Experimental Setting
To generate the test data for identifying Hiroden blog
entries, we used the 1,748 blog entries collected in Section
IV-A. Then, we manually identified Hiroden blog entries
among 1,748 blog entries. The number of manually
identified Hiroden blog entries is shown in Table I.
TABLE I.

NUMBER OF MANUALLY IDENTIFIED HIRODEN BLOGENTRIES

Hiroden Blog Entries
568

Other
1,180

Total
1,748

B. Machine Learning and Evaluation
We performed a four-fold cross validation test. We used
Tiny SVM (http://chasen.org/~taku/software/TinySVM/)
software as the machine-learning package and used precision,
recall and F-measure as evaluation measures, calculated asfollows:
(1)

(2)
(3)
To confirm our method, we also examined a baseline
method that identified all 1,748 blog entries as Hiroden blog
entries.
C. Results and Discussion
Table II shows the experimental results. The recall scoreby our method was 0.355 lower than that of the baseline
method. However, our method improved the precision and Fmeasure scores of the baseline method by 0.499 and 0.233,
respectively. We have thus confirmed the effectiveness of
our method for identifying Hiroden blog entries.
TABLE II.

Our method
Baseline method

IDENTIFICATION OF HIRODEN BLOG ENTRIESPrecision
(%)
82.4
32.5

Recall
(%)
64.5
100.0

F-measure
(%)
72.4
49.1

We now discuss the low recall of our method. A typicalreason for low recall is the lack of cues. For the machinelearning, we used manually selected cues, as described in
Section IV-B. Most cues are words related to Hiroden, suchas “広電” (Hiroden) or “市電” (streetcar). However, various
cues were needed for collecting more Hiroden blog entries,
for example, the names of the restaurants, tourist spots, andsouvenir shops that often appear in Hiroden blog entries. To
improve the coverage of cues, a statistical approach, such as
applying n-gram statistics to a larger blog corpus, will be
required.

VI.

CONCLUSION

In this paper, we propose a method for collecting
Hiroden blog entries from a blog database. The task isdivided into two steps: (1) collection of blog entries, and (2)identification of Hiroden blog entries. To investigate theeffectiveness of our method, we conducted someexperiments. From the experimental results, we obtained
precision of 82.4%, recall of 64.5% and F-measure of 72.4%
in automatic identification of Hiroden blog entries. Finally,
we have constructed a system for providing travel
information along Hiroden streetcar lines.
In our future work, we will classify Hiroden blog entries
into some categories of travel, such as “sightseeing,” “eating,”
or “staying.” Users can then check Hiroden blog entries
more efficiently.
At present, our system considers only Hiroden blog
entries as travel information. In our future work, we willcollect Yahoo! Answers (http://answers.yahoo.com/) andnews Websites related to Hiroden. Yahoo! Answers(formerly known as Yahoo! Q&A) is a community-driven
question-and-answer (Q&A) site. Such information is usefulfor travelers. Our system will provide rich information.

[2]

[3]

[4]

[5]

[6]

[7]

[8]

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