Automatic Compilation of Travel Informationfrom Automatically Identified Travel Blogs
Hidetsugu anba
Graduate School of Information
Sciences, Hiroshima City University
nanba@hiroshima-cu.ac.jp
Takahiro Ozaki
School of Information Sciences,Hiroshima City University

Aya Ishino
Graduate School of Information
Sciences, Hiroshima City University
ishino@ls.info.hiroshimacu.ac.jp
Abstract
In this paper, we propose a method for compilingtravel information automatically. For the compilation, we focus on travel blogs, which are defined as travel journals written by bloggers indiary form. We consider that travel blogs are auseful information source for obtaining travel
information, because many bloggers' travel experiences are written in this form. Therefore, weidentified travel blogs in a blog database and extracted travel information from them. We haveconfirmed the effectiveness of our method byexperiment. For the identification of travel blogs,
we obtained scores of 38.1% for Recall and86.7% for Precision. In the extraction of travel
information from travel blogs, we obtained
74.0% for Precision at the top 100 extracted local
products, thereby confirming that travel blogs area useful source of travel information.

1IntroductionTravel guidebooks and portal sites provided bytour companies and governmental tourist boards
are useful sources of information about travel.
However, it is costly and time consuming tocompile travel information for all tourist spots
and to keep them up to date manually. Therefore
we have studied the automatic compilation oftravel information.
For the compilation, we focused on travel
blogs, which are defined as travel journals writ-Haruka Taguma
School of Information Sciences,
Hiroshima City University
Daisuke Kobayashi
Graduate School of Information Sciences,
Hiroshima City University
kobayashi@ls.info.hiroshimacu.ac.jpToshiyuki Takezawa
Graduate School of Information Sciences,Hiroshima City University
takezawa@hiroshima-cu.ac.jp
ten by bloggers in diary form. Travel blogs areconsidered a useful information source for obtaining travel information, because many bloggers' travel experiences are written in this form.
Therefore, we identified travel blogs in a blog
database, and extracted travel information fromthem.
Travel information in travel blogs is also useful for recommending information that ismatched to the each traveler. Recently, several
methods that identify bloggers' attributes such as
residential area (Yasuda et al., 2006), gender,and age (Ikeda et al., 2008, Schler et al., 2006),
have been proposed. By combining this researchwith travel information extracted from travel
blogs, it is possible to recommend a local product that is popular among females, for example,
or a travel spot, where young people often visit.
The remainder of this paper is organized asfollows. Section 2 describes related work. Section 3 describes our method. To investigate theeffectiveness of our method, we conducted someexperiments, and Section 4 reports the experimental results. We present some conclusions inSection 5.

2

Related Work

Both'www.travelblog.org'and'travel.blogmura.com' are portal sites for travel
blogs. At these sites, travel blogs are manuallyregistered by bloggers themselves, and the blogs
are classified by their destinations. However,there are many more travel blogs in the blogos-

phere. Aiming to construct an exhaustive database of travel blogs, we have studied the automatic identification of travel blogs.
GeoCLEF 1 is the cross-language geographic
retrieval track run as part of the Cross Language
Evaluation Forum (CLEF), and has been operating since 2005 (Gey et al., 2005). The goal ofthis task was to retrieve news articles relevant toparticular aspects of geographic information,such as 'wine regions around the rivers in Europe'. In our work, we focused on travel blogs
instead of news articles, because bloggers' travel
experiences tend to be written in travel blogs.

each 416 cue phrase, such as '旅行 (travel)', 'ツ
ア ー (tour)', and ' 出 発 (departure)', and thenumber of location names in each entry2.

3[cue phrase] (416 in total)
1: contain, 0:not containparsing direction

10travel
tour
departure
train
visited0001001100001011101001010111111100010k

Identification of Travel Blogs

http://ir.shef.ac.uk/geoclef/

returntag

Automatic Compilation of Travel Information

Blog entries that contain cue phrases, such as'travel', 'sightseeing', or 'tour', have a high degreeof probability of being travel blogs. However,
not every travel blog contains such cue phrases.
For example, if a blogger writes his/her journey
to Norway in multiple blog entries, it might state'We traveled to Norway' in the first entry, while
only writing 'We ate wild sheep!' in the secondentry. In this case, because the second entry does
not contain any expressions related to travel, it isdifficult to identify that the second entry is a travel blog. Therefore, we focus not only on eachentry but also on its surrounding entries for theidentification of travel blogs.
We formulated the identification of travel
blogs as a sequence-labeling problem, and solved
it using machine learning. For the machine learning method, we examined the Conditional Random Fields (CRF) method, whose empirical success has been reported recently in the field ofnatural language processing. The CRF-based method identifies the class of each entry. Features
and tags are given in the CRF method as follows:(1) the k tags occur before a target entry, (2) kfeatures occur before a target entry, and (3) kfeatures follow a target entry (see Figure 1). We
used the value of k=4, which was determined in apilot study. Here, we used the following features
for machine learning: whether an entry contains

traveltarget

The task of compiling travel information is divided into two steps: (1) identification of travel
blogs and (2) extraction of travel informationfrom them. We explain these steps in Sections
3.1 and 3.2.
3.1

traveldeparture

blog entries

Figure 1: Features and tags given to the CRF

3.2

Extraction of Travel Information fromBlogs

We extracted pairs comprising a location nameand a local product from travel blogs, whichwere identified in the previous step. For the efficient extraction of travel information, we employed a bootstrapping method. Firstly, we prepared 482 location-name/and local-product pairs
as seeds for the bootstrapping. These pairs wereobtained automatically from a 'Web Japanese Ngram' database 3 provided by Google, Inc. The
database comprises N-grams (N=1–7) extracted
from 20 billion of Japanese sentences on the web.
We applied a pattern '[地名]名物「[名物]」'([slot of 'location name'] local product 「[slot of'local product name']」) to the database, and extracted location names and local products fromeach corresponding slot, thereby obtaining the482 pairs.
Secondly, we applied a machine learningbased information extraction technique to thetravel blogs identified in the previous step, andobtained new pairs. In this step, we prepared2

We used CaboCha software for the identification oflocations.
http://chasen.org/~taku/software/cabocha/
3
http://www.gsk.or.jp/catalog/GSK2007C/catalog.html

training data for the machine learning in the following three steps.
1.

2.

3.

Select 200 sentences that contain both a location name and a local product from the482 pairs. Then automatically create 200
tagged sentences, to which 'location' and'product' tags are assigned.
Prepare another 200 sentences that contain
only a location name. 4 Then create 200
tagged sentences, to which the 'location' tag
is assigned.
Apply machine learning to the 400 tagged
sentences, and obtain a system that automatically annotates 'location' and 'product' tags
to given sentences.

As a machine learning method, we used the CRF.
In the same way as in the previous step, theCRF-based method identifies the class of eachword in a given sentence. Features and tags aregiven in the CRF method as follows: (1) the ktags occur before a target word, (2) k features
occur before a target word, and (3) k features
follow a target word. We used the value of k=2,
which was determined in a pilot study. We use
the following six features for machine learning.






4

A word.
Its part of speech5.
Whether the word is a quotation mark.
Whether the word is a cue word, such as '名物', '名産', '特産' (local product), '銘菓'(famous confection), or '土産' (souvenir).
Whether the word is a surface case.
Whether the word is frequently used in thenames of local products or souvenirs, suchas 'cake' or 'noodle'.

Experiments

We conducted two experiments: (1) identification of travel blogs, and (2) extraction of travel
information from blogs. We reported on them in
Sections 4.1 and 4.2.
4.1

Identification of Travel Blogs

Data sets and experimental settings

4In our pilot study, we did not use these negative cases in machine learning at first, and obtained low precision values, because our system attempted to extract
local products from all sentences containing location
names in travel blogs.
5In this step, we also identified location names automatically using the CaboCha software.

We randomly selected 4,914 blog entries writtenby 317 authors from about 1,100,000 entries
written in Japanese. Then we manually identified
travel blogs in 4,914 entries. As a result, 420 entries were identified as travel blogs. Then we
performed a four-fold cross-validation test. For
the machine-learning package, we used CRF++6software. For evaluation measures, we used Recall and Precision scores.
Alternatives
In order to confirm the validity of our sequencelabeling-based approach, we also examined
another method, which identifies travel blogs
using features in each blog entry only (without
using features in its surrounding entries).
Results and discussions
Table 1 shows the experimental results. As
shown in the table, our method improved thePrecision value by 26.2%, while decreasing the
Recall value by 13.0%. In our research, Precision
is more important than Recall, because low Precision in this step causes low Precision in thenext step.
our methodbaseline method

Recall38.1
51.1

Precision
86.7
60.5

Table 1: Identification of travel blogs

Our method could not identify 266 of the travel blogs. We randomly selected 50 entries fromthese 266, and analysed the errors. Among the 50errors, 25 cases (50%) were caused by the lack ofcue phrases. For the machine learning, we used
manually selected cue phrases. To increase thenumber of cue phrases, a statistical approach will
be required. For example, applying n-grams toautomatically identified travel blogs is one suchapproach. Among the 50 errors, 5 entries (10%)
were too short (fewer than four sentences) to beidentified by our method.
Our method mistakenly identified 26 entries astravel blogs. A typical error is that bloggers
wrote non-travel entries among a series of travel
blogs. In this case, the non-travel entries wereidentified as travel blogs.
4.2

Extraction of Travel Information from
Blogs

Data sets and experimental settings
To confirm that travel blogs are a useful information source for the extraction of travel information, we extracted travel information using thefollowing three information sources.
6

http://www.chasen.org/~taku/software/CRF++/

Travel blogs (our method): 80,000 sentences in 17,268 travel blogs, which wereautomatically identified from 1,100,000 entries using the method described in Section
3.1.
Generic blogs: 80,000 sentences from
1,100,000 blog entries.
Generic webs: 80,000 sentences from
470M web sentences (Kawahara and Kurohashi, 2006).



We extracted travel information (locationname/local-product pairs) from each information
source, and ranked them by their frequencies.
Evaluation
We used the Precision value for the top-ranked
travel information defined by the following equation as the evaluation measure. We calculated
Precision values from the top 5 to the top 100 atintervals of 5.
The number of correctly extracted
location-name / local-productpairs
Precision=The number of extracted
location-name / local-productpairs

Results and discussions
Figure 2 shows the experimental results. As
shown in the figure, the generic blog method obtained higher Precision values than the generic
web method, especially at higher ranks. Our method (travel blog) was much better than the generic blog method, which indicates that travel
blogs are a useful information source for the extraction of travel information.

travel blog (our method)
41generic blog
15generic web
7
Table 2: The number of local products that each method newly extracted

5Conclusion

In this paper, we proposed a method for identifying travel blogs from a blog database, and extracting travel information from them. In theidentification of travel blogs, we obtained of
38.1% for Recall and 86.7% for Precision. In theextraction of travel information from travel blogs,
we obtained 74.0% for Precision with the top
100 extracted local products.

References
Fredric C. Gey, Ray R. Larson, Mark Sanderson, Hideo Joho, Paul Clough, and Vivien Petras. 2005.
GeoCLEF: The CLEF 2005 Cross-Language Geographic Information Retrieval Track Overview.
Lecture otes in Computer Science, LNCS4022,
pp.908-919.
Daisuke Ikeda, Hiroya Takamura, and Manabu Okumura. 2008. Semi-Supervised Learning for BlogClassification. Proceedings of the 23rd AAAI Conference on Artificial Intelligence, pp.1156-1161.

10.8Precision

the table, 41 local products were newly extracted
from travel blogs, while 15 and 7 were extracted
from generic blogs and generic webs, respectively. These results also indicate the effectiveness oftravel blogs as a source for travel information.
A typical error among the top 100 results forour method was that store names were mistakenly extracted. Here, most of these stores sell localproducts. To ameliorate this problem, extractionof pairs of local products and the stores that sellthem is also required.

0.6

Daisuke Kawahara and Sadao Kurohashi. 2006. A
Fully-Lexicalized Probabilistic Model for Japanese
Syntactic and Case Structure Analysis. Proceedingsof the Human Language Technology Conference ofthe orth American Chapter of the Association for
Computational Linguistics, pp.176-183.

0.40.205 15 25 35 45 55 65 75 85 95
Top ntravel blog
generic blog
generic web

Jonathan Schler, Moshe Koppel, Shlomo Argamon,
and James Pennebaker. 2006. Effects of age andgender on blogging. Proceedings of AAAI Symposium on Computational Approaches for Analyzing
Weblogs, pp.199-205.

Figure 2: Precision values at top n for the extractionof travel information

Norihito Yasuda, Tsutomu Hirao, Jun Suzuki, andHideki Isozaki. 2006. Identifying bloggers' residential areas. Proceedings of AAAI Spring Symposiumon Computational Approaches for Analyzing Weblogs, pp.231-236.

Table 2 shows the number of local products,
which were not contained in a list of products
from the Google N-gram database. As shown in