Classification and Visualization of Travel Blog Entries
Based on Types of Tourism
Naoki Shibata1,
Hiroto Shinoda1,
Hidetsugu Nanba2,
Aya Ishino3,
Toshiyuki Takezawa1
1 Graduate School of Information Sciences, Hiroshima City University, Hiroshima, Japan
2 Faculty of Science and Engineering, Chuo University, Tokyo, Japan
3 Faculty of Media Business, Hiroshima University of Economics, Hiroshima, Japan1{shibata, shinoda, takezawa}@ls.info.hiroshima-cu.ac.jp
2nanba@kc.chuo-u.ac.jp
3ay-ishino@hue.ac.jp

Abstract. We propose a method for classifying travel blog entries into one ormore tourism types among six predetermined types by using textual and image
information in each entry. Together with this information, we use Wikipedia entries, which are automatically linked from each travel blog entry by entity-linking
technology, because information beneficial for classifying blog entries is often
mentioned in Wikipedia entries, and we combine this information by using a
deep-learning-based method. We conducted an experiment with a neural network
using three types of input data. Using the Sparse Composite Document Vector(SCDV) technique, we obtained precision, recall, and F-measure scores of 0.743,
0.217, and 0.336, respectively. We also conducted ensemble learning by using
SCDV and support vector machines (SVM), and obtained precision, recall, andF-measure scores of 0.807, 0.179, and 0.293, respectively. Finally, we constructed a system that enables travelers to look for travel blog entries from a map
in terms of tourism type.
Keywords: Types of Tourism, Travel Blog, Document Classification, Wikification.

1

Introduction

In recent years, tourism has expanded into various types. For example, tourism for the
purpose of health recovery is called “health tourism,” and that for the purpose of experiencing sports is called “sports tourism.” If the types of tourism could be automaticallyclassified for travel blog entries, it would enable people to determine what types areavailable at tourist spots around the world. It would also be possible to recommend
tourist sites and travel plans on the basis of tourism types. In this study, we define six

2types of tourism and propose a method for classifying a large amount of travel blog
entries into these types automatically by using machine learning that considers multiple
input data.
At present, many people around the world use social networking services (SNSs). If auser has information to share, they will post it to SNSs. The tourism industry is no
exception. We can help the development of the tourism industry by extracting useful
information from such enormous data on SNSs. In particular, many travel blog entries
have detailed information such as experiences and photos taken at a tourist site. In thisstudy, we analyze travel blog entries.
The remainder of this paper is organized as follows. In Section 2, we discuss related
work. Section 3 describes our method. To investigate the effectiveness of our method,
we conducted experiments, whose results are reported in Section 4. Section 5 shows
the behavior of a system we developed in terms of snapshots. We present our conclusion in Section 6.

2

Related WorkIn this study, we use Wikification [1, 2] methods in addition to textual and image information. Wikification is the linking of text and Wikipedia entities. We classify travel
blog entries automatically on the basis of tourism types by using this information. Furthermore, we map the classification results.
To enable the distributed representation of documents, Iyyer et al. [3] proposed a model
called a “deep averaging network” (DAN) that converts words contained in a document
into vectors and uses the average of them for classification. Furthermore, Mekala et al.
[4] proposed the Sparse Composite Document Vector (SCDV) technique as an alternative method of generating document vectors. SCDV takes word vectors into consideration with Gaussian mixture modelling (GMM) and inverse document frequency (IDF)
and uses the average of the generated word vectors as a document vector. A schematic
diagram of SCDV is shown in Figure 1. In this figure, classification is performed foreach cluster (a–e) by using GMM, and a document vector is generated by averaging theresult and the word vector in consideration of the IDF. In this study, we use this SCDVin the proposed method and a baseline method in an experiment on classifying travel
blog entries on the basis of tourism types.
There have been a number of studies related to document classification regarding tourism [5, 6]. Takahashi et al. [5] used travel tweets from Twitter and proposed a methodof classifying a traveler’s behaviors, i.e., what the traveler is doing, into “sightseeing,”“business,” “eating,” and “shopping.” In addition to that, Fujii et al. proposed a methodof classifying a traveler’s behaviors, i.e. what the traveler is doing, into “buy,” “eat,”“experience,” “stay,” and “see” from travel blog entries written in English [6] and Japanese [7]. In their study, although there is some relevance to the classification focusing

3

on types of tourism in our study, it is basically considered to be another viewpoint.
Combining Fujii et al.’s classification with our proposed one based on tourism types
could potentially enable more detailed searches, such as examining information related
to “eat” content with “cultural tourism.”

Fig. 1. Schematic of SCDV (Mekala et al. [4]) (compiled by author)

To enable travel information recommendations, Xiong et al. [8] constructed a personalized online hotel marketing recommendation system by extracting hotel characteristic
factors and analyzing customers’ browsing and purchasing behaviors. Also, Iinuma etal. [9] proposed a method for generating a summary of multiple travel blog entries that
contain images and constructed a system. The system classifies the travel blog entries,
which were collected by Nanbas’ method (Nanba et al. [10]), by using Fujii et al.’s
method.

3Classification of Travel Blog Entries on the Basis of
Tourism Types

3.1

Definition of Tourism Types

At present, there are many types of tourism, and most of them have no strict definitions.
We selected and defined six tourism types on the basis of the ease of automaticallyclarifying them. Table 1 shows the different types, their definitions, and examples. We
automatically classify travel blog entries written in English to clarify the tourism typesof travelers on the basis of the six types.

4
Table 1. Definition and example of the types of tourism (own material)

types of tourism
infrastructure andhard tourism
health tourism
sports tourism
green tourism
heritage tourism
cultural tourism

3.2

definition
Tourism for modern buildings
and recreational facilities.
Tourism aimed at health recovery, health maintenance,and health improvement.
Tourism aimed at experiencing or watching sports.
Tourism aimed at interactingwith nature.
Tourism for historic buildingssuch as world heritage sites.
Tourism for life, culture, ethnicity, and tradition of areas.

examplesbridges, dams, theme parks,
shopping malls, aquariums
religious pilgrimages, hotsprings, hiking, trekking
MLB, Soccer World Cup,
Olympics
agriculturalexperience,
fruit hunting, picnics
World Heritage sites, national treasures, castles
festivals, interchange withlocal people

Classification of Travel Blog Entries Based on Types of Tourism

In this study, we classify travel blog entries automatically on the basis of the types of
tourism shown in Section 3.1. In this section, we explain the policy of automatic classification and the automatic classification of travel blog entries with machine learning.
3.2.1

Automatic Classification Policy

We analyze text, images, and Wikification results from travel blog entries and automatically classify them into tourism types by using the results of the analysis. Among them,textual information is the most important. For example, if a blog entry contained thephrase “I went to Mont Saint Michel, a UNESCO World Heritage Site,” this is considered to be an example of “heritage tourism” because the blog text contains the expression “UNESCO World Heritage Site.”
A disadvantage of text analysis is that misinterpreted context could produce inaccurate
results. For example, if a blog entry contained the phrase “I wanted to ski but I could
not do it,” although the word “ski” is present, the entry would not be related to “sports
tourism.” However, if an image related to skiing is in the blog entry, it can be assumed
that the author was skiing. In this study, we used the Google Cloud Vision API 1 fordetecting objects in images. The API can classify images into thousands of categories,
detect objects/faces, etc. We used words that obtained the object detection results inaddition to textual information when classifying blog entries.
Classification requires external knowledge of what can be read from text or images. For
example, if a blog entry contained the phrase “I saw Pyramid in Egypt. It was very big,”1

https://cloud.google.com/vision/?hl=en

5this blog entry is classified as “heritage tourism” because the Pyramid is a World Heritage Site. However, there is no information in the text. To judge this correctly as “heritage tourism,” the inclusion of external knowledge such as information from Wikipedia is required. For this, we used the Google Cloud Natural Language API2. When text
is sent to the cloud via the API, in addition to part-of-speech tagging, parsing, and lexical expression extraction, Wikification is also performed. By using the results of Wikification, we can obtain the information that the Pyramid is a World Heritage Site fromthe linked page of Wikipedia. Accordingly, in this study, we aim to achieve more accurate classification by using Wikification information as external knowledge in addition to the information from text and images.
3.2.2

Automatic Classification of Travel Blog Entries Using Machine
Learning

In this study, we first prepare input data from the text, images, and Wikification resultsof a travel blog entry. Objects are detected in images, and words included in the linked
Wikipedia abstract (the first paragraph) from the Wikification results are extracted. After that, we construct classifiers in consideration of each piece of input data. Since theclassifiers are binary classifications, blog entries that do not fall into the six tourism
types are classified as “other.” A schematic of the classifier is shown in Figure 2, where
each piece of input data is processed, and the results are combined in a hidden layer.

Fig. 2. Schematic of proposed classifier (authors’ own figure)This classifier integrates the analysis results of each piece of input data in the hidden
layer. This is why the number of words included in each is significantly different. For
example, in a travel blog, when the text includes 1,000 words, the object detection result
includes 30 words, and the abstract of Wikipedia includes 100 words, so the number of2

https://cloud.google.com/natural-language/?hl=en

6words differs; thus, the influence of input data with a small number of words is reduced.
For this reason, analysis results for each piece of input data are integrated in the hidden
layer in this study.

4

Experiments

We conducted an experiment by using TravelBlog3, one of the largest travel blog websites. It hosted over 700,000 blog entries in 2013. The aim of this experiment was toachieve a high precision accuracy as the number of blog entries is very high.
4.1

Experimental Conditions

Each blog entry was classified into one or more tourism types among six predetermined
types manually and used as training data and test data for machine learning. A breakdown of the results classified manually is shown in Table 2. First, we originally definednine tourism types, i.e. dark tourism, contents tourism, study tourism, and the six tourism types listed in Table 1. Second, we manually classified 2,017 randomly-selectedtravel blog entries. This data was created by a student, whose major is International
Studies and is good at speaking English. Third, due to a lack of blog entries related todark, contents, and study tourism (less than 30), we discarded those types because it
was insufficient for machine learning. Finally, we obtained 2,017 travel blog entries
and categorized them into the remaining tourism types as shown in Table 2. Also, itshould be noted that 227 entries were classified with multiple types of tourism and
1,227 could not be classified at all.
Table 2. Breakdown of results classified manually (own material)

types of tourism
infrastructure and hard tourism
health tourism
sports tourism
green tourism
heritage tourism
cultural tourism
targeted for classification

number
168
125
57
453
198
49
2,017

For the distributed expression of words, we used a pre-trained model provided by
Google, the Word2Vec model of 300-dimension vectors 4 . This well-known model
learned from the Google News dataset about 100 billion words, which is much larger
than the TravelBlog corpus. The object detection function used the Google Cloud Vision API described in Section 3.2.1. We conducted classification by taking into accountthe results of Wikification, which, as stated above, is a method of obtaining a distributed
3
4

https://www.travelblog.org/
https://github.com/mmihaltz/word2vec-GoogleNews-vectors

7expression from a Wikipedia abstract. Also, we experimented with ensemble voting
methods with the same weights. For the ensemble (proposed) method, it used threeclassifiers: SCDV(txt+img+wiki) (proposed), SVM(txt), and SVM(img). For the ensemble (baseline) method, we used three classifiers: SCDV(txt), SVM(txt), andSVM(img). We applied the radial basis function (RBF) kernel to both SVM(txt) andSVM(img). We obtained the best epoch values using the optimization function“RMSpropGraves.” For the activation function, we adopted ReLU in the hidden layerand softmax in the output layer.
The evaluation was performed by 5-fold cross validation to decrease problems likeoverfitting or selection bias, and precision, recall, and F-measure scores were used. The
cross-validation process was repeated five times, with each of the five subsamples (thenumber of data samples are 403, 403, 403, 403, and 405) being used as the test datawith the remaining four as training data. The five results can then be averaged to produce a single estimation. To calculate these, we used a micro average to take into account the bias in the number of data for each type of tourism. The precision and recall
formulas are shown below.
𝒑𝒓𝒆𝒄𝒊𝒔𝒊𝒐𝒏 =𝒓𝒆𝒄𝒂𝒍𝒍 =𝑻𝒓𝒂𝒗𝒆𝒍 𝒃𝒍𝒐𝒈 𝒆𝒏𝒕𝒓𝒊𝒆𝒔 𝒄𝒍𝒂𝒔𝒔𝒊𝒇𝒊𝒆𝒅 𝒄𝒐𝒓𝒓𝒆𝒄𝒕𝒍𝒚
𝑻𝒓𝒂𝒗𝒆𝒍 𝒃𝒍𝒐𝒈 𝒆𝒏𝒕𝒓𝒊𝒆𝒔 𝒄𝒍𝒂𝒔𝒔𝒊𝒇𝒊𝒆𝒅 𝒂𝒔 𝒕𝒐𝒖𝒓𝒊𝒔𝒎 𝒕𝒚𝒑𝒆𝒔

𝑻𝒓𝒂𝒗𝒆𝒍 𝒃𝒍𝒐𝒈 𝒆𝒏𝒕𝒓𝒊𝒆𝒔 𝒄𝒍𝒂𝒔𝒔𝒊𝒇𝒊𝒆𝒅 𝒄𝒐𝒓𝒓𝒆𝒄𝒕𝒍𝒚
𝑻𝒓𝒂𝒗𝒆𝒍 𝒃𝒍𝒐𝒈 𝒆𝒏𝒕𝒓𝒊𝒆𝒔 𝒍𝒂𝒃𝒆𝒍𝒍𝒆𝒅 𝒎𝒂𝒏𝒖𝒂𝒍𝒍𝒚 𝒂𝒔 𝒕𝒐𝒖𝒓𝒊𝒔𝒎 𝒕𝒚𝒑𝒆𝒔

Generally, a trade-off between precision and recall is necessary. In our study, high precision is more important even if recall is low, and because more than 230,000 travel
blog entries are available, this will resolve the low recall. In this experiment, we used
the following three proposed methods (Table 3) and six baseline methods (Table 4).
We conducted t-tests (p < 0.01), which confirmed that there were significant differences
between SCDV(txt) and SCDV(txt+img) (proposed), and between SCDV(txt+img) and
SCDV(txt+img+wiki).
Table 3. Proposed methods and features for use (own material)

Ensemble (proposed)
 SCDV(txt+img+wiki)
 SVM(txt) SVM(img)
SCDV(txt+img+wiki)
SCDV(txt+img)

text

image

Wikification

○

○

○

○
○

○
○

○
−

8
Table 4. Baseline methods and features for use (own material)

Ensemble (baseline) SCDV(txt) SVM(txt) SVM(img)
SCDV(txt)
SVM(txt)
SCDV(img)
SVM(img)
SCDV(wiki)
4.2

text

image

Wikification

○

○

−

○
○
−
−
−

−
−
○
○
−

−
−
−
−
○

Results and Discussion

Table 5 shows the experimental results of each baseline and the proposed methods described in Section 4.1. The highest precision of 0.807 was obtained with Ensemble(proposed). Also, the highest recall and F-measure scores of 0.272 and 0.385 were obtained with SVM(txt).
Compared with SVM(img), which had the highest precision among the baseline methods, Ensemble (proposed) produced better results for precision, recall, and F-measure,indicating that it is more effective. In terms of different input data, the proposed
SCDV(txt+img) obtained a higher precision than the baseline SCDV(txt) and
SCDV(img). Furthermore, the proposed SCDV(txt+img+wiki) obtained a higher precision than the proposed SCDV(txt+img), and baseline SCDV(txt), SCDV(img), and
SCDV(wiki).
Table 6 shows the number of blog entries changed from ensemble (baseline) method toensemble (proposed) method. The reason only two types are shown in this table is that
there was no difference between outputs of the two methods. Focusing on green tourism, the number of misclassifications decreased, while the number of correctly classified blog entries also decreased. It appears that textual information is more important
than image and Wikipedia information when classifying travel blog entries as green
tourism. On the other hand, the number of correctly classified blog entries increased inheritage tourism. In this case, image and Wikipedia information are useful, and they
contribute to improve the recall value. Thus, increasing the number of inputs is valid
when classifying travel blog entries.

9
Table 5. Experimental results (micro average) (own material)

methodEnsemble (proposed)
 SCDV(txt+img+wiki)
 SVM(txt) SVM(img)
SCDV(txt+img+wiki) (proposed)

epoch

precision

recallF-measure

-

0.807

0.179

0.293

30

0.752

0.218

0.338

SCDV(txt+img) (proposed)
Ensemble (baseline) SCDV(txt) SVM(txt) SVM(img)
SCDV(txt) (baseline)

30

0.729

0.227

0.347

-

0.747

0.216

0.335

20

0.639

0.169

0.268

SVM(txt) (baseline)

-

0.654

0.272

0.385

SCDV(img) (baseline)

10

0.725

0.140

0.235

SVM(img) (baseline)

-

0.788

0.170

0.279

SCDV(wiki) (baseline)

30

0.528

0.116

0.191

Table 6. The number of blog entries changed from ensemble (baseline) method to ensemble(proposed) method (own material)

5

green.

heritage.

correctly classified into “a tourism type”

25

9incorrectly classified into “a tourism type”

15

1

correctly classified into “not a tourism type”

47

1

incorrectly classified into “not a tourism type”

69

4

total number of blog entries (as show in Table 2)

453

198

System Behavior

In this section, we introduce our system’s behavior in terms of the travel blog entries
collected and classified by our proposed method. The system intuitively reveals thefeatures of the tourism types for each tourist site. The procedure for visualization is asfollows.
(1) Collect travel blog entries, and extract text and images from entries.
(2) Analyze images by using the Google Cloud Vision API, and estimate object detection and location information.
(3) Perform Wikification on text by using the Google Cloud Natural Language API.
(4) Collect Wikipedia entity information with the results obtained by Wikificationand extract abstracts of linked Wikipedia articles.

10(5) Classify on the basis of tourism types automatically using the obtained text, image
analysis results, and abstracts of Wikipedia.
(6) Visualize data on a Google Earth map by using the location information obtainedby the image analysis results. If multiple location information references can beextracted, the first one extracted is adopted.

Fig. 3. Travel blog entries classified on the basis of tourism types (Egypt) (compiled by author)

We collected about 230,000 random travel blog entries from TravelBlog, and used
24,023 entries whose location information could be estimated for classification. The
system is shown in Figure 3. This figure illustrates Egypt and its surrounding. We usedicons to indicate type, such as a green house for “infrastructure and hard tourism,” a
hot spring for “health tourism,” a bike for “sports tourism,” a tree for “green tourism,”
the rocks for “heritage tourism,” and a temple for “cultural tourism.” If the user clicks
an icon, the corresponding travel blog entry is shown on the map. From this figure, we
can confirm many icons of the rocks, “heritage tourism” around the Nile River. In addition, the users of this system can find other tourism types information, such as “green
tourism,” “health tourism,” and “infrastructure and hard tourism.” Thus, this systemenables to look up the information about tourism types that are useful for the users.

11

6

Conclusion

In this study, we proposed a method for automatically classifying travel blog entries
into one or more tourism types among six predetermined types in consideration of text
and images found in them and Wikipedia information. For images, we used the Google
Cloud Vision API to detect objects and adopted the results as classification features.
For Wikipedia information, we performed Wikification by using the Google Cloud Natural Language API, and we used the word sets included in the abstracts of linked Wikipedia articles as classification features. The experimental results show that a precision
score of 0.807 was obtained for ensemble learning, which combined
SCDV(txt+img+wiki), SVM(txt) and SVM(img).
For the visualization system, we classified 24,023 travel blog entries and visualized
travel blob data on a map by using Google Earth. The proposed system enables analysts
to investigate traveler behavior (Wenger et al. [11]) and marketing (Mack et al. [12])via massive numbers of travel blog entries. However, currently, the system assumes
travel blog entries written in English as input. Our future work is to expand to blog
entries written in other languages.

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