Travellers’ Behaviour Analysis Based on AutomaticallyIdentified Attributes from Travel Blog Entries
Kazuki Fujii1, Hidetsugu Nanba1, Toshiyuki Takezawa1, Aya Ishino2,
Manabu Okumura3 and Yohei Kurata4
1

Hiroshima City University, 3-4-1 Ozuka-higashi, Asaminami-ku,
Hiroshima, 731-3194, Japan{fujii, nanba Travellers’ Behaviour Analysis Based on Automatically Identified Attributes from Travel Blog Entries, takezawa}@ls.info.hiroshima-cu.ac.jp
2
Hiroshima University of Economics, 5-37-1 Gion, Asaminami-ku,
Hiroshima 731-0192, Japan
ay-ishino@hue.ac.jp
3Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku,
Yokohama, 226-8503, Japanoku@pi.titech.ac.jp
4
Tokyo Metropolitan University, 1-1 Minamiosawa, Hachioji, Tokyo, 192-0397, Japanykurata@tmu.ac.jp

Abstract. We propose a method to analyse travellers’ behaviour using automatically identified travellers’ attributes, such as gender and language, from travel
blog entries. We consider that travel blog entries are a useful information sourcefor obtaining travel information, because many bloggers’ describe their travel
experiences in them. Several studies have analysed travellers’ behaviour using
travel blog entries. However, they used a small number of manually identified
travellers’ (bloggers’) attributes. In our work, we identify travellers’ attributes
automatically using natural language processing techniques, and conduct alarge-scale travellers’ behaviour analysis.
Keywords: travel blog, behaviour analysis, travellers’ attributes,

1

Introduction

Being aware of travellers’ needs is crucial for tourism planning. Traditionally, suchanalyses were conducted using questionnaires, but these are costly and timeconsuming. Recently, travel blog entries have been used instead of questionnaires. In
travel blog entries, various travellers’ experiences and opinions are described, andthey can help identify travellers’ needs. For example, Wenger [1] analysed travel blog
entries written by travellers visiting Austria, and found that many females visited
Austria to enjoy dining experiences. However, few blog entries were used in thisanalysis, because the analysis was conducted manually. In this paper, we propose amethod for analysing travellers’ behaviour from vast numbers of blog entries using
natural language techniques. In our approach, we identify travellers’ (bloggers’) atCopyright © 2016, the Workshops of the 14th Pacific Rim International Conference on Artificial Intelligence, SIIT, Thammasat University, Pathumthani, Thailand. All Rights Reserved.

tributes automatically using method based on machine learning. Then, we use theattributes to characterize travellers’ behaviour.
The remainder of this paper is organized as follows. Section 2 discusses related
work. Section 3 describes our method of identifying travellers’ (bloggers’) attributes
in travel blog entries. To investigate the effectiveness of our method, we conducted
some experiments, and Section 4 reports the experimental results. Using our method,
we conducted travellers’ behaviour analysis, and Section 5 reports the results. We
present some conclusions in Section 6.

2

Related Work

Questionnaires have been frequently used in marketing surveys to determine tourist
policies. Xia et al. [2] asked 464 visitors to Phillip Island about their gender, tourist
spots they planned to visit, and their residence, and conducted behaviour analysis
using decision-tree methods. Similarly, Jonsson et al. [3] also conducted a questionnaire investigation of 163 visitors to a Caribbean island, and analysed travellers’ behaviour. However, it is costly and quite time-consuming to conduct questionnaire
investigations. Therefore, we aim to analyse travellers’ behaviour automatically fromtravel blog entries, because there are many descriptions of travellers’ experiences andmany opinions in travel blog entries.
The number of studies using travel blog entries for travellers’ behaviour analysis
has been increasing. Mack et al. [4] and Akehurst [5] reported that travel blog entries
are useful for tourism marketing, although they are not as reliable as traditional wordof mouth. Li and Wang [6] analysed impressions of China from Taiwanese travellers
using travel blog entries in a Chinese travel portal site. They manually classified blog
entries to several categories, such as “scenery”, “purchase”, and “stay”, and analysed
entries in each category. They reported that the impressions of hot springs were good,and suggested that these hot springs should be promoted as a major tourist attractionin China. Classifying travel blog entries for travellers’ behaviour analysis is a common point with our work. However, our work classifies entries automatically, whichenables us to conduct a larger-scale analysis.
In related research using travel blog entries, Wenger [1] conducted travellers’ behaviour analysis based on travellers’ attributes, such as gender and age. They used thedataset of TravelBlog1, in which some bloggers reveal their attributes in their profiles.
However, few bloggers explicitly describe these attributes. We therefore identify
bloggers’ attributes automatically, and use them for travellers’ behaviour analysis.
Recently, several methods for identifying bloggers’ attributes, such as gender, age[7, 9], and residential area [8] have been proposed. We identify travellers’ gender
based on Ikeda’s method. With respect to travel-related documents, Saeki et al. [10]identified travellers’ languages in each geotagged tweet using a Java languagedetection2 program (langdetect), and conducted foreign travellers’ behaviour analysis
12

https://www.travelblog.org/
https://code.google.com/p/language-detection/

in Japan. In the same way, we also apply the program to travel blog entries, and use
them for travellers’ behaviour analysis.

3

Automatic Identification of Travellers’ Attributes

We aim to analyse travellers’ behaviour based on the travellers’ gender, language, andbehaviour using the TravelBlog dataset. Unfortunately, this information is not explicitly written in the dataset. Therefore, we identify attributes automatically using natural
language processing techniques. We explain how to identify a blogger’s (a traveller’s)
gender, language, and behaviour (content type of each blog entry) in Sections 3.1, 3.2,
and 3.3, respectively.
3.1

Identification of Blogger’s GenderTo identify bloggers’ genders, we propose three methods: (1) semi-supervised learning approach based on Ikeda’s method [7] (SSL), (2) cue-word-based method (CUE),
and (3) a combination of the CUE and SSL methods.
Ikeda et al. (2008) assumed that each blogger has a writing style based on his/her
attributes, such as gender and generation. They identified these attributes from two
kinds of data: (i) a small number of blog entries in which bloggers explicitly describe
their attributes; and (ii) a large number of entries with no explicit attributes using asemi-supervised technique. They experimentally confirmed that their approach obtained an accuracy score of 0.890 for the identification of bloggers’ genders, while asupervised approach using the data (i) obtained 0.760. Therefore, we examine thesemi-supervised approach based on the Ikeda’s method (SSL).
In addition to the SSL method, we also examine a cue-word-based method. Thismethod focuses on the differences in words that male and female bloggers use. We
created two cue word lists by collecting words that are frequently used in blog entries
written by male or female bloggers, and used them for the identification of bloggers’
genders (CUE).
As our third approach, we examine the combination of the SSL and CUE methods.
The basic procedure is the same as that for the SSL method, except that the
SSL+CUE method does not use all words in blog entries, only those that appear in
two cue-word lists. We believed that using the cue-word lists could identify the features of blog entries more clearly, and would improve the SSL method. In the experiment in Section 4, we will report that our SSL+CUE method did actually improve the
SSL method.
3.2

Identification of Travellers’ Languages

Many entries in the TravelBlog database were written in English, but other languages,
such as French and German, are also used. We focus on the languages that bloggers
used, and analyse the travellers’ behaviours in each city they visited in terms of theirlanguages. This analysis is particularly useful for tourism policy. For example, if wefind that there are many visitors who use French in a particular city, then it is effective
for the city authorities to provide signs in French.
To identify which language a blogger uses in a blog entry, we use the langdetect
program. This library automatically estimates probabilities of each language for agiven text. We attempt to identify travellers’ languages in two different ways: (1)identify the language that has the highest probability score (Top method); and (2)identify all languages having probabilities higher than a threshold value (Thresholdmethod). This threshold value was determined in a pilot study.
3.3

Identification of Content TypeEven when travellers visit the same destination, the purpose of the visit is not always
the same. Some travellers might visit tourist spots as their primary purpose, whileothers might visit to enjoy local dining. We aim to make the visitors’ purposes clear
by identifying the content type of each blog entry, as shown in Table 1. These types
were originally proposed by Fujii et al. [11], and they devised a system that identifies
one or more content types relevant to a given blog entry written in Japanese. In their
method, they first collected cue words that are useful for identifying content types
using the information gain (IG) method. Second, they applied Support Vector Machine (SVM) to identify content types using cue words as features for the SVM. In
our work, we developed a system that can identify content type for a given blog entry
written in English using two different methods. In the first method, we collect 100 cue
words for each content type using IG and apply the SVM, using the same procedureas that used by Ishino et al. for Japanese blog entries. In the second method, we employ a machine translation method (MT) by combining the first method with a Japanese identifier devised by Ishino et al. In the MT method, we translate an English
blog entry into Japanese using the Microsoft Translator API3, and then identify its
content type using the Japanese identifier (MT). Then, we use the result as one of thefeatures of SVM, and identify the content type of the given English blog entry(IG+MT).
Table 1. Content types and their descriptions
Content type
Watch
Experience
Buy
Dine
Stay

3

Criterion
Sightseeing for watching enjoyment
Experience (scuba diving, dance)
Shopping or souvenir stores
Drinking and dining
Accommodation

https://datamarket.azure.com/dataset/bing/microsofttranslator

4

Experiments

To confirm the effectiveness of our method, we conducted three experiments: (1)identification of blogger’s gender; (2) identification of the language used; and (3)identification of the content type of each blog entry. We describe them in Sections
4.1, 4.2, and 4.3, respectively. In the experiments, we used blog entries from theTravelBlog dataset, which we mentioned in Section 3.
4.1

Identification of Traveller’s Gender

4.1.1
Experimental Settings
Data
We used 228 bloggers for this experiment, 77 males and 151 females. We obtained
the gender information from each blogger’s profile. In this experiment, we used blog
entries written in English.
Machine Learning and Evaluation Measure
We employed SVM with a linear kernel for machine learning, and conducted two-foldcross-validation. We used accuracy determined by the following equation as an evaluation measure.
(#)*+$ -. /$%0+11+$2
3ℎ-2+ 5+67+$2 %$+ "-$$+"/1& 87+6/8.8+7!""#$%"& =(#)*+$ -. /$%0+11+$2

Alternatives
We examined the following four methods.
l Baseline: Identify all bloggers as female.
l SSL: Semi-supervised learning based on Ikeda’s method [7].
l CUE: Use two different cue word lists as features for machine learning. Theselists were created by collecting approximately the top 100 words that appeared inblogs written by males or females.
l SSL+CUE: A combination of the SSL method and the CUE method. When we
conduct the SSL method, we use words in the above two lists as features for machine learning.
Results
We show the experimental results in Table 2. Of the four methods, our method
SSL+CUE obtained the best accuracy score.
We expected Ikeda’s method to obtain a high accuracy score, because they also
identified bloggers’ genders, and obtained a score of 0.890. Unfortunately, their
method obtained 0.667 with our data, which is much smaller than their experimental
result. We consider that this must be because of the different characteristics of the twodatasets. Ikeda’s experiment used blog entries about various topics, such as sport,
politics, automobiles, beauty salons, and sweets, while we only used blog entries

about travel. As a result, Ikeda’s method could not capture the gender-related featuresof blog entries, and did not work well in our dataset. On the other hand, the two cue
word lists assisted to capture the gender-related features of blog entries, and as a result, SSL+CUE outperformed SSL.
Table 2. Evaluation results for the identification of bloggers’ genders
MethodBaseline
SSL
CUE
SSL+CUE
4.2

Accuracy
0.662 (151/228)
0.667 (152/228)
0.776 (177/228)
0.877 (195/228)

Identification of Traveller’s Language

4.2.1
Experimental Settings
Data
We manually identified the language that each of 109 bloggers used in their blog
entries, and used these for our experiment. The statistics are shown in Table 3. Notethat some bloggers used more than one language.
Table 3. The number of bloggers for each language
Language
EnglishGermanSpanishDutch
FrenchDanishItalianJapanese

Bloggers
83
10
9
9
6
5
2
2

Language
Portuguese
SwedishAfrikaans
Hungarian
FinnishSloveneRomanian

Bloggers
1
1
1
1
1
1
1

Evaluation Measure
We used recall and precision as evaluation measures. Of these, we consider that precision is more important, because a low precision score causes inaccurate analysis, as
we will describe in Section 5. In addition, as the number of blog entries has been increasing, the low recall can be improved.
Alternatives
For the identification of the languages that each blogger uses, we used the langdtect
program, with the following two methods.

llTop: languages having the highest probability among automatically identified
languages by the langdetect program.
Threshold: languages with probabilities higher than a threshold value, which
was determined in a pilot study.

Results
We show the experimental results in Table 4. The Top method obtained the higher
precision score of the two methods. In the analysis in Section 5, we used the Top
method.
Table 4. Evaluation results for the identification of bloggers’ languages
MethodPrecision
Recall
Top
0.972
0.797
Threshold
0.887
0.887
4.3

Identification of Content Types of Each Blog Entry

Data
We examined using 660 travel blog entries, all written in English. For these entries,
we assigned content types. A summary of the data is shown in Table 5. Here, morethan one content type was assigned to several entries, so the total number of entries inTable 6 is larger than 660.
Table 5. Summary of the data using in the examination of content typeidentification
Content
Buy
Dine
Experience Stay
Watch
Other
type
Entries
30
97
143
61
316
155
Machine Learning and Evaluation Measures.
We employed SVM with a linear kernel. We evaluated our methods and a baseline
method by recall and precision.
Alternatives
We examined the following methods.
l IG: SVM-based approach with cue words, which were identified using
information gain method.
l MT: Machine translation-based approach using Microsoft translator
and Japanese content-type identifiers [11].
l IG+MT: SVM-based approach using cue words and the MT result asfeatures.
l Baseline: SVM-based approach using all words as features.

Results
We show the experimental results in Table 6. Our method, IG+MT, outperformed thebaseline method by 0.449 points, confirming the effectiveness of our method.
Table 6. Evaluation results for identification of blog entry type
MethodPrecision
Recall
IG
0.574
0.296
MT
0.458
0.406
IG+MT
0.597
0.336
Baseline
0.148
0.702

5

Analysis of Travel Blogs Based on Bloggers’ Attributes

We analysed 7,490 blog entries focusing on bloggers’ attributes and the content typesof each blog entry. These blog entries were written by 1,302 bloggers who had visited
Japan.
5.1

Basic Statistics of Visitors to Japan Based on the Automatically Identified
Attributes

First, we show some basic statistics of foreign visitors to Japan based on the attributesof visitors and content types of blog entries.
GenderWe show the number of bloggers for each gender in Table 7.
Table 7. The number of automatically identified bloggers for each gender
GenderTravellers
Male
513
Female789
Language
We show the number of bloggers for each language in Table 8. We removed the English-speaking travellers from the results, because, the number of English-speaking
travellers in TravelBlog is much larger than others, as we mentioned previously. From
the results in Table 8, French and German are most often used after English.
Table 8. The number of automatically identified travellers for each language
Language
Travellers
French22
German13
Dutch
10
Spanish7
Finnish7

Content TypeWe analysed the content types of the 7,490 blog entries automatically. We show theresults in Table 9. The primary purpose of visitors to Japan is watching. On the other
hand, only one blog entry had the content type are “buy”.
Table 9. Automatically identified content types of blog entries
Content type
Entries
Buy
1
Dine
1134
Experience
315
Stay
319
Watch
3213
5.2

Analysis Based on Attributes of Travellers and Content Types

Using automatically identified travellers’ attributes, we conducted travellers’ behaviour analysis. In this report, we mainly focused on Japan as a test case, but it is possible to conduct the same analysis in any city in any country, because we have alreadyidentified all travellers’ attributes.
5.2.1
Analysis Based on Travellers’ Languages and Content Types of Travel
Blog Entries
We analysed travellers’ languages in each prefecture. The top three prefectures withthe largest numbers of languages used by travellers are shown in Table 10.
Table 10. Top three prefectures that the number of languages
Prefecture
Visitors
Languages
Tokyo
68
18
Kyoto
31
13
Hiroshima
21
10
Among these three prefectures, in the following, we compare Kyoto and Hiroshima interms of travellers’ languages and content types of travel blog entries. Fig. 1 shows
the proportion of blog entries with content type “watch” as a heat map. Red cities
indicate that they have more “watch” entries. In this figure, we also show travellers’languages as bar charts4. From the results in Fig. 1, we found that the proportion of“watch” entries was higher in Hiroshima and Kyoto than in the other prefectures.
We read the “watch” entries in Kyoto and Hiroshima, and found that many Frenchspeaking travellers visited world heritage locations in both prefectures. From the result, we further investigated the ratio of French-speaking travellers for each prefecture. We show the results in Fig. 2.
4

When we created this chart, we eliminated English-speaking travellers, because most of
the travellers (bloggers) in the TravelBlog dataset used English.

Fig. 1. The proportion of “watch” travel blog entries and travellers’ languages
for each prefecture

Tokyo
Kyoto
Hiroshima
Okayama
Nara
Gifu
Shizuoka
Okayama
Oita
Hokkaido
Other
Fig. 2. The proportion of French-speaking travellers among all
From the results in Fig. 2, we found that approximately 30% of French-speaking
travellers visited either Kyoto or Hiroshima. We also found that French-speaking
travellers tend to visit world heritage locations in other prefectures. From these results, we can conclude that promotion of world heritage locations and providing signs
in French are more important in these areas.
5.2.2
Analysis Based on Travellers’ Gender and Content Types of Travel Blog
Entries
To compare the different travel purposes between males and females, we calculated
the proportions of each content type for each gender in Japan. We show the results in
Fig. 3 (a). The results indicate that there are small differences between males andfemales. However, when we focus on particular areas, we found obvious differences
between genders. Fig. 3 (b) shows the proportions of each content type for each gender in Ehime prefecture. The figure shows that the secondary purpose of males who
visit Ehime is to dine, while that of females is to experience. Ehime prefecture is famous for its hot springs, and we found that many female visitors enjoyed them. Fromthese results, we can conclude that the purposes of visiting a particular area are notalways the same for each gender, and different promotions are required. For example,
in Ehime prefecture, providing toiletries and bath towels will please females, which
seems likely to increase the number of female visitors to Ehime prefecture.

Female(Japan)Male(Japan)(a) Japan

Male(Ehime)Female(Ehime)(b) Ehime prefecture
Fig. 3. Proportions of content types of travel blog entries for each gender

6.

Conclusion

In this paper, we have analysed travellers’ behaviour using travel blog entries. For thisanalysis, we used travellers’ attributes identified automatically from travel blog entries. To identify gender, our method, SSL+CUE, obtained an accuracy score of
0.877, which outperformed the baseline method by 0.223. To identify languages, weused the langdetect program, and confirmed that it obtained precision of 0.972 andrecall of 0.797. To identify the content type of each blog entry, our method, IG+MT,
obtained precision of 0.597 and recall of 0.327. Using 7,490 travel blog entries withthese attributes, we conducted behaviour analysis of 1,302 travellers, who visited
Japan. We were able to derive useful information for tourist authorities.

7.

Acknowledgements

This study was carried out under support of Ministry of Internal Affairs and Communications' SCOPE (Strategic Information and Communications R&D Promotion Programme).

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