Extraction and Visualization of Trend Informationfrom Newspaper Articles and Blogs
Hidetsugu Nanba

Nao Okuda

Manabu Okumura

Hiroshima City University
3-4-1 Ozukahigashi,
Hiroshima 731-3194 JAPAN

Hiroshima Elpida Memory, Inc.
7-10 Yoshikawa Kogyo Danchi,
Higashi Hiroshima 739-0198
JAPANTokyo Institute of Technology
4259 Nagatsuta, Yokohama
226-8503 JAPAN

Phone: +81-82-830-1584
nanba@hiroshima-cu.ac.jp

Abstract
Trend information is a summarization of temporal
statistical data, such as changes in product prices andsales. We propose a method for extracting trend information from multiple newspaper articles and blogs,
and visualizing the information as graphs. As target
texts for extraction of trend information, the MuST
(Multimodal Summarization for Trend Information)
workshop focuses on newspaper articles. In addition
to newspapers, we focus on blogs, because useful information for analysing trend information is often
written in blogs, such as the reasons for increases/decreases of statistics and the impact of increases/decreases of statistics on society. To extract
trend information, we extract temporal expressions
and statistical values, and we devised methods forboth operations. To investigate the effectiveness of
our methods, we conducted some experiments. We
obtained a recall of 6.3% and precision of 31.3% fornewspaper articles, and a recall of 44.8% and precision of 60.3% for blogs. From the error analysis, wefound that most errors in newspaper articles werecaused by misconversion of temporal expressions
such as “同年” (the same year) or “前月” (the previous month), into “YYYY-MM-DD” form, althoughtemporal expressions were detected correctly. In contrast to newspaper articles, there are few temporal
expressions in blogs for which resolution is required,such as “同日” (the same day) or “前月” (the previous month). As a result, recall and precision for blogs
are higher than those for newspaper articles.

Keywords: Trend Information, Visualization,
Newspaper article, Blog.

1

Introduction

Trend information is a kind of summarization of
temporal statistical data, such as changes in productprices and sales. We propose a method for extracting
trend information from newspaper articles and blogs,
and visualizing it as graphs.
Analysis of trend information has been studied fora long time in various fields, such as stock price prediction and temperature forecasting. A traditional approach is to apply time-series analysis to predict fu-

Phone: +81-45-924-5295
oku@pi.titech.ac.jp

ture values of the time-series variable [Doornik andHendry 2001]. Recently, Gruhl proposed a method topredict the sales rank of a book in Amazon1 [Gruhl etal. 2005] based on the number of blogs that mention
the book. Although these approaches are useful for
predicting statistics, they cannot contribute to a
deeper understanding of the statistics.
We focus not only on statistical data but also on theinformation that enables us to understand the statistical data. Therefore, we have studied extraction andvisualization of trend information from texts.
As target texts for extraction of trend information,
the MuST (Multimodal Summarization for Trend Information) workshop [Kato et al. 2005, 2007] focuseson newspaper articles, and several studies have donein this field [Murata et al. 2006, Watanabe and Kobayashi 2006]. In addition to newspapers, we focus on
blogs, because useful information for analysing trends
is often written in blogs, such as the reasons for increases/decreases of statistics and the impact of increases/decreases of statistics on society.
The remainder of this paper is organized as follows.
Section 2 describes trend information in newspaper
articles and blogs. Section 3 explains how to extract
trend information from them. Section 4 overviews our
system. To investigate the effectiveness of our
method, we conducted some examinations and Section 5 reports on these. Section 6 shows system behaviour. We present some conclusions in Section 7.

2

Description of Trend Information in
Newspaper Articles and Blogs

Descriptions in newspaper articles and blogs canprovide a deeper understanding of trend information.
Figures 1 and 2 show sentences extracted from anewspaper article and a blog entry, respectively. Bothexamples describe trend information. Statistical values and descriptions of the values are shown as underlines and wavy lines, respectively.
In Figure 1, the author of the article described why
the yen fell. In Figure 2, the author of the blog entry
wrote on the effects of the increase of gasoline prices.
The former example is an objective fact, while the1

http://www.amazon.com

latter is subjective (or supposition). Thus, descriptions
about trend information in newspapers and blogs aredifferent.
八日の東京外国為替市場は、日本経済の先行き不透明感やアジアの経済危機の悪化を懸念した
円売り・ドル買いの動きが一段と強まり、円は
一時、一ドル＝一四〇円七三銭まで下落した。
(The yen fell to 140.73 yen against the U.S. dollar on
June 3 as an increasing number of market players soldthe yen and bought the dollar because of uncertainty
surrounding the nation’s economy and fears of an
escalation of the Asian economic crisis.)Table 1 Classification of blog entries
Entries that cite newspaper
articles in full, with no original content
Entries that partially cite
newspaper articles
Entries that mention theirinformation source as newspaper articles, without citingthem
Entries with unspecified information sources

Fraction (%)
25.8 (80/310)

4.8 (15/310)
50.0 (155/310)

19.4 (60/310)(June 8, 1998, Yomiuri newspaper article)
Figure 1 Example of a newspaper article thatmentions trend information
日本ではレギュラーガソリンの平均価格が 136
円/1 リッターと過去最高水準を記録して話題になっていますが，アメリカ本国でも 1 ガロン
2.88 ドル（約 84 円/1 リッター）と，これまた最高記録を更新しそうな勢い．2 年と 4 ヶ月でほぼ 2 倍になったアメリカのガソリン価格が，このハマーをはじめとした大排気量大型モデルの販売不振に直結しているのは間違いないみた
いですね．
(The average price of regular gasoline reached 136
yen/litre in Japan, hovered at this record level, andgot into the news in Japan. On the other hand, theaverage price in U.S. was 2.88 dollars per gallon(about 84 yen/litre), which was a near-record level.
The price of gasoline has almost doubled in the last
two years and four months, and there is almost no
doubt that this directly affected the sales of highemission vehicles, such as Hummer.)
Figure 2 Example of a blog entry that mentions
trend information
Many blog entries containing trend information are
based on some information source, such as newspapers or TV news, so it is not obvious that all blog entries contain original material. To investigate theoriginality of descriptions in blogs, we classified blog
entries according to the degree of citation of newspaper articles. The result is shown in Table 1. As can beseen from Table 1, about 75% of blog entries containoriginal contents, and more than half of blog entries
do not cite newspaper articles. In other words, these
entries are fully written by the blog authors.

3

Extraction of Trend Information

The task of extracting trend information can be divided into two subtasks: (1) extraction of temporal
expressions and (2) extraction of statistical values
from texts. We describe these subtasks in Sections 3.1
and 3.2, respectively.

3.1

Extraction of Temporal Expressions

We use CaboCha2 [Kudo and Matsumoto 2003] forextracting temporal expressions from texts. CaboCha
is a statistical syntactic parser for Japanese texts, andalso identifies eight kinds of named entity, such asdate, organization, and location, in texts. Here, not all
temporal expressions in texts are used to generate agraph. For example, there are three temporal expressions in the sentence in Figure 3, but “1994” is the
only expression that is used in generating a graph of“the number of births for one year”.
一九九四年の年間出生数は前年より四万七千人
も多い百二十三万五千人を記録し，二十一年ぶ
りに大幅増に転じた．
(The number of births recorded in 1994 was
1,235,000, which was 47,000 greater than in the previous year, and this significant increase was the firstin 21 years.)
Figure 3 Example of a sentence that contains morethan one temporal expression
To eliminate unrelated temporal expressions fromtexts, we focus on some cue phrases, some of which
are shown in Table 2. In the table, cue phrases areunderlined. If these cue phrases appear before or after
temporal expressions, they are eliminated.
Table 2 Examples of cue phrases to eliminate unrelated temporal expressions
前年より (in comparison with the previous year)
10 年ぶり (for the first time in 10 years)
昨年以来 (since last year)
3 年連続で (for the third year in a row)2

http://chasen.org/~taku/software/cabocha/

In the next stage, the extracted temporal expressions in the prior stage are converted into a specificformat “<DATE>YYYY-MM-DD</DATE>”, where
YYYY, MM, and DD indicate year, month, and day,respectively. For example, if “昨日” (yesterday) is
extracted from a text written on January 25, 2007,
then the expression “yesterday” is replaced by“<DATE>2007-01-24</DATE>”. If the exact date is
not provided, such as “昨年 12 月” (last December),this expression is converted as “<DATE>2006-12??</DATE>”.

3.2

Extraction of Statistical Values

We extract statistical values in the following four
steps:
1.
2.
3.
4.

Split a sentence into separate statistical values;
Annotate “NUM” and “UNIT” tags;
Eliminate unrelated statistical values;
Extract statistical values.

(Step 1) Split a sentence into separate statisticalvaluesSome sentences contain more than one statisticalvalue, for example:
今日のレギュラーガソリンは１３０円，ハイ
オクは１５０円だった．
(Today’s price of regular gasoline was 130 yen, andthat of premium gasoline was 150 yen.)
From this sentence, “150 yen” might be extracted
mistakenly as the price of regular gasoline, or “130yen” as that of premium gasoline. We therefore split
sentences for the statistical values. First, we analyse
the dependency structure of the sentence using CaboCha. Second, we integrate two bunsetsus (segments)that have a modification relation and are adjacent toeach other. We explain this process using the example
shown in Figure 4. This figure is the result from
CaboCha for the sentence given above. “Chunk” tagswith ID numbers are assigned to each bunsetsu. Here,attribute values in each chunk tag indicate ID numbers of bunsetsus that have a modification relation.
Bunsetsus 0 and 1 have a modification relation, andare adjacent to each other. Therefore, these bunsetsus
are integrated. By conducting the process repeatedly
to the end of the sentence, it is split into two parts: (1)“Today’s price of regular gasoline was 130 yen,” and(2) “and that of premium gasoline was 150 yen”.
(Step 2) Annotate “NUM” and “UNIT” tags
We add “NUM” and “UNIT” tags to all candidate
statistical values using the pattern “number + (nounphrase | postfix | counter suffix)”. For example, NUM
and UNIT tags are annotated for an expression “150円 ” (yen) as “<NUM>150</NUM><UNIT> 円</UNIT>”.

<chunk id="0" link="1">今日の(Today’s)</chunk><chunk id="1" link="2">レギュラーガソリンは(price of regular gasoline was)</chunk><chunk id="2" link="4">130 円，(130 yen, )</chunk><chunk id="3" link="4">ハイオクは(and that of premium gasoline was)</chunk><chunk id="4" link="–1">150 円だった．(150yen.)</chunk><chunk>今日のレギュラーガソリンは 130 円，(Today’s regular gasoline was 130 yen, )</chunk><chunk>ハイオクは 150 円だった． (and premium
gasoline was 150 yen.)</chunk>
Figure 4 Splitting a sentence for statistical values(Step 3) Eliminate unrelated statistical values
Not all expressions assigned “NUM” and “UNIT”
tags are used to generate a graph of trend information.
For example, there are two statistical values in thesentence in Figure 5, but “９２円” (92 yen) is the
only expression that is used to generate a graph.
石油情報センターが２３日発表した給油所石油製品市況調査によると，６月のガソリン価格は全国平均でレギュラー１リットル当たり<NUM>９２</NUM><UNIT>円</UNIT>となり，前月比で<NUM>２</NUM><UNIT>円</UNIT>上昇した．
(According to the Oil Information Centre’s survey ofmarket conditions for products sold through service
stations on (June) 23, the price of gasoline reached anational
averageof<NUM>92</NUM><UNIT>yen</UNIT>
per
litre,
regular;<NUM>2</NUM><UNIT>yen</UNIT> higher than
the average price of last month)(June 24, 1999, Mainichi newspaper article)
Figure 5 Example of an analysis of a newspaper
articleIn the same way as extraction of temporal expression, we focus on some cue phrases to eliminate unrelated statistical values from texts, some of which are
shown in Table 3. Here, cue phrases are shown as
underlines. If they appear before or after statisticalvalues, they are eliminated.
Table 3 Example of cue phrases to eliminate unrelated statistical values
2 円高い (2 yen higher)
昨年比 45.2%上昇 (up 45.2% from the previous
year)
0.28 パーセント下落 (down 0.28 percent)(Step 4) Extract statistical values
All statistical values are extracted when a predefined keyword (the name of the statistic) and unit appear in the same integrated bunsetsu. For example,“130 yen” is extracted from the sentence in Figure 3,

when “レギュラー” (regular) and “円” (yen) were
given to the system in advance.

4

Table 4 Topics, newspaper articles and blog entries used in the experiments
TopicsSystem Configuration
The system configuration is shown in Figure 6.
Input

Search Texts

Text
Collection

Extract Trend Information

Generate a Graph

Numberof Blogs

22

68

16

265

26

225

20
6
37

578
192
195

20

57

17

108

Shipment volume ofbeer
Vehicle shipments
Sales of communication devices
Gasoline prices
Attendance at movies
Nikkei stock average
Domestic shipment
volume of personal
computers
Cabinet approval rate

5.2
OutputNumber ofNews Articles(MuST data)

Experimental Method

We evaluate our method using the following equations.

Figure 6 System configuration
Input
The system input is one or more keywords, whichindicate a topic (e.g. “cabinet approval rate”) and aunit (e.g. “dollar”, “%”).
Search Texts
The system searches texts using keywords from a text
collection.
Extract Trend Information
The system extracts temporal expressions and statistical values from texts searched in the prior stage.
Generate a Graph
The system generates a graph using a Perl module,“GD Graph”3.
OutputThe system outputs the graph and the documents.

5

Experiments

To investigate the effectiveness of the method
given in Section 3, we conducted some examinations.

5.1

Data

We used the MuST data [Kato et al. 2007] and blog
data in the examinations. The MuST data consists ofnewspaper articles about 27 topics written in Japanese,
and were extracted from the Mainichi newspaper database for the years 1998 and 1999. Among these 27
topics, we manually selected eight that were alsomentioned in blogs. The blog data was collected using
blogWatcher4 [Nanno 2004], a Japanese blog searchengine. All the blog entries were written in 2006. Details of the data are shown in Table 4.
34

http://search.cpan.org/dist/GDGraph/
http://blogwatcher.pi.titech.ac.jp/

5.3

Results

The experimental results for newspaper articles andfor blogs are shown in Tables 5 and 6, respectively.
We calculated recall and precision for each of thefollowing three cases: (1) temporal expressions extraction, (2) statistical values extraction, and (3) pairsof temporal expressions and statistical values extraction.
Table 5 Results of trend information extraction
from newspaper articles
Time
Statistics
Time andStatistics

Recall (%)
6.3 (31/491)
19.6 (96/491)

Precision (%)
31.3 (31/99)
97.0 (96/99)

6.3 (31/491)

31.3 (31/99)

Table 6 Results of trend information extraction
from blogs
Time
Statistics
Time andStatistics

Recall (%)
55.5 (239/431)
59.9 (258/431)

Precision (%)
74.7 (239/320)
80.6 (258/320)

44.8 (193/431)

60.3 (193/320)5.4

Discussion

generated automatically using GD Graph, a Perl module.

As can be seen from Tables 5 and 6, extraction
from newspaper articles is worse than from blogs. In Shareparticular, both recall and precision of temporal ex- (%)
pression extraction from newspapers are very low.
From the error analysis, we found that most errors
were caused by misconversion of temporal expression
into “YYYY-MM-DD” form, although temporal expressions were detected correctly in most cases. Figure 7 shows a typical example of such errors.
総市場のシェアは，キリンビールが３８・５％で首位を守り，前月はキリンと１・３ポイント差に迫ったアサヒは３４・７％．
(Kirin’s share in the total market is 38.5%, and thecompany maintained the top ranking, while Asahi’sshare is 34.7%, although Asahi got within 1.3% of
Kirin in the previous month.)(March 12, 1999, Mainichi newspaper article)

Year
Figure 8 A graph of “Asahi’s share” in the national beer market

7
総市場のシェアは，キリンビールが<NUM>38.5</NUM><UNIT>％</UNIT>で首位を守り，<DATE>1999-2-??</DATE>はキリンと<NUM>1.3</NUM><UNIT>ポイント</UNIT>差に迫ったアサヒは<NUM>34.7</NUM><UNIT>％</UNIT>．
(Kirin’s share in the total market is<NUM>38.5</NUM> <UNIT>%</UNIT>, and thecompany maintained the top ranking, while Asahi’sshare is <NUM>34.7</NUM> <UNIT>%</UNIT>,
although Asahi got within<NUM>1.3</NUM><UNIT>%</UNIT> of Kirin in<DATE>2006-02-??</DATE>.)
Figure 7 Example of error
In this example, “前月” (the previous month) is mistakenly converted into “1999-2-??”, because the system inferred that “the previous month” was the monthprevious to March, when this article was written.
However, “the previous month” indicates January
1999, because this article describes the shares of beersales in February.
In contrast to newspaper articles, there are few
temporal expressions in blogs for which resolution isrequired, such as “同日” (the same day) or “前月”
(the previous month). As a result, recall and precisionof blogs are higher than those of newspaper articles.

6

System Behaviour

Figure 8 shows a graph of “Asahi’s share” in thenational beer market. We used “アサヒ” (Asahi) and“%” as a keyword and a unit, respectively. Four statistical values, which were extracted from Mainichi
newspaper articles in 1998 and 1999, are shown. The
x-axis and y-axis indicate “year” and “share in thenational beer market”, respectively. The graph wasConclusions

We have proposed a method for extracting statistical values and temporal expressions from newspaper
articles and blogs, and visualizing them as graphs. In
our experiments, we obtained a recall of 6.3% and a
precision of 31.3% for newspaper articles, and a recallof 44.8% and a precision of 60.3% for blogs. Fromthe error analysis, we found that most errors in newspaper articles were caused by misconversion of temporal expression, such as “同年” (the same year) or“前月” (the previous month), into “YYYY-MM-DD”
form, although temporal expressions were detected
correctly in most cases. In contrast to newspaper articles, there are few temporal expressions in blogs that
require resolution, such as “同日” (the same day) or“前月” (the previous month). As a result, recall andprecision of blogs are higher than those for newspaper
articles.

8

Acknowledgements

The authors would like to express their gratitude to
the organizers of the MuST for providing the data set.
This work was supported by Grant-in-Aid for Young
Scientists (B) from the Ministry of Education, Culture,
Sports, Science and Technology of Japan.

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