Exploratory analysis of stylistic characteristics in Japanese
Q&A communities
Takafumi Suzuki 1, Shuntaro Kawamura 2, Akiko Aizawa 1
1

National Institute of Informatics - 2-1-2 Hitotsubashi - Chiyoda-ku - Tokyo - Japan
2

University of Tokyo - 7-3-1 - Bunkyo-ku - Hongo - Tokyo – Japan

Abstract
This study is an exploratory analysis of the stylistic characteristics of text submitted to Japanese Q&A communities.
Along with the development of social media, Q&A communities are attracting much scholarly attention as
important resources for analyzing online communication. In Q&A communities, people freely submit questions
and answers; questions are classiﬁed into subject categories; and the best answers are selected. In this study, we
analyze the stylistic characteristics of three types of submission, i.e., questions, best answers, and normal answers,
in two different subject categories, i.e., ‘personal computers and related devices’ and ‘love and human relations
advice’. The results show that the textual styles clearly distinguished these six classes of text and clariﬁed theirrespective characteristics. Our ﬁndings provide useful knowledge about how people differ in their communicationstyles regarding subject categories and on how people select communication styles. This study will contribute toresearch into discovering current online communication styles.

Keywords: computational stylistics, online communication styles, Japanese Q&A communities

1. Introduction
Through the development of the Web, various new texts media have appeared (Aitchison andLewis, 2003). In particular, text in social media such as Wikis, blogs, and SNS are produced by
the users themselves, reflect their users’ interests, and reveal new styles of online communication.
The textual characteristics of such media should be useful for tracking changes in language
usage and current communication styles on the Web, especially in the context of Japanese.
Among the many social media, Q&A communities where people freely submit questions and answers
online are attracting much scholarly attention. In Q&A communities, questions are classified
into subject categories, and the best answers are selected by some criteria 1. Thus, the text submissions
provide us with fruitful examples of how people differ in their communication styles regarding
subject categories and of how people select communication styles to fit their circumstances.
To analyze the text submissions, we focus on their styles. Style, i.e., textual characteristics
independent on the content of the text, is ‘how it is mentioned in the text’ (Argamon et al.,2007), and knowledge of style has various new applications, such as authorship profiling,
sentiment analysis, and computational sociolinguistics, as well as conventional applications,such as authorship attribution and genre discrimination (Argamon et al., 2007; Koppel et al.,2009; Stamatatos, 2009; Suzuki, 2009). Styles are useful for determining, for example, the1Regarding the data we used in this study, all the best answers were selected by questionnaire.

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EXPLORATORY ANALYSIS OF STYLISTIC CHARACTERISTICS IN JAPANESE Q&A COMMUNITIES

author’s personality, feelings, sentiments; thus, we thought they would be good for analyzing
communication styles of Q&A communities.
This study constitutes an exploratory analysis of the stylistic characteristics of texts submitted
to Japanese Q&A communities. We compare two different types of submission, factual
questions 2 and personal advice questions 3 (Harper et al., 2008; Miura and Kawamura, 2008),
which are typical, yet completely different types of questions. By analyzing the stylistic
characteristics of questions, best answers, and normal answers, of these two categories, we
can see how people vary their communication styles across subject categories and how people
select communication styles. Note that we conducted only an exploratory analysis, because
there are few studies focusing on the textual characteristics of Q&A communities, especiallyin Japanese. By analyzing the styles of the submissions, we tried to derive knowledge on how
people communicate with each other online; such knowledge is becoming more and more
important since people are spending more time in virtual spaces. Our study also provides
fundamental knowledge for many IR and NLP tasks, e.g., good answer estimation, automaticparaphrasing, and automatic conversation generation.

2. Data
We used Yahoo! Chiebukuro (Japanese version of Yahoo! answers) data provided to National
Institute of Informatics by Yahoo Japan Corporation. This data includes 3,116,009 questions,
3.116.008 best answers, 10.361.777 normal answers that were submitted during the period from
April 2004 to October 2005. All the submissions are classified into subject categories.
We selected two categories for our analyses, ‘personal computers and peripheral devices’ (PC),and ‘love and human relationships advice’ (LH). The former category is a typical one that
includes factual questions, whereas the latter category is a typical one that includes personal
advice questions. We collected texts of questions (Q), best answers (BA), normal answers (NA),per month 4 and applied morphological analysis using MeCab 5, a Japanese morphologicalanalysis system. We assigned parts-of-speech tags by using MeCab and calculated the numberof tokens per submission and frequencies of function words per text.
As features, we used the bag-of-words of the relative frequencies of function words, i.e.,functional nouns (noun-dependent and noun-pronominal), adnominals, conjunctions, particles
and auxiliary verbs. As function words independent of the content represent the affect, genre,
register and personality of the texts (Argamon et al., 2007), and are effective for sociolinguistic
analysis as well as stylistic text classification (Garcia and Martin, 2007; Grieve, 2007; Suzuki,2009), they are appropriate features for our purpose. In Japanese, particles and auxiliary verbs arestrongly related to the modality of the text (Otsuka et al., 2007) and adnominals, conjunctions,and some particles represent the logicality and readability of the text (c.f., Otsuka et al., 2007;
Tuldava, 1993), while some functional nouns can represent explanation patterns.
It is better for our purpose to use deeper-order part-of-speech tags of particles 6 and the stemming
version of auxiliary verbs as they facilitate more meaningful interpretations 7. Tab. 1 lists the
2
3
4

5
6
7

They are, in other words, questions that have certain answers.
They are, in other words, questions that have no certain answers.
Text is usually analyzed on a per submission basis for many NLP and IR tasks, but our purpose here is to clarify
the basic stylistic characteristics of six categories; thus, it is better to use the texts per month.
mecab.sourceforge.net.
‘Case particles’ or ‘conjunctive particles’, etc.
A particle can have different meanings when it is used in different second-order parts-of-speech, while the

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number of submissions and the total number of tokens and types of function words in the six
categories, while Tab. 2 lists the respective number of tokens and types for each part-of-speech.
length of postings
PC
LH

Q
BA
NA
Q
BA
NA

number of postings

mean

s.d.

c.v.

171,867
171,848
302,839
210,124
210,105
1,206,457

52.69
55.93
37.59
70.79
68.11
45.57

34.68
43.36
31.85
53.03
48.72
37.83

65.82
77.53
84.72
74.90
71.54
83.00

Table 1: Basic data of our corpora 1

PC
Q
		
BA
		
NA
LH
Q
		
BA
		
NA
all		

functional nouns

conjunctions

N

V(N)

N

V(N)

N

V(N)

509,223
378,526
490,155
1,119,160
1,038,136
3,901,410
7,436,610

192
205
211
213
219
225
227

40,422
52,66957,24589,206
96,351
345,121681,014

120
132
136
133
137
146
149

62,99264,19775,677163,096
143,042538,682
1,047,686

63
75
75
78
86
94
97

particles
PC
Q
		
BA
		
NA
LH
Q
		
BA
		
NA
all		

aux. Verbs

adnominals

functional words

N

V(N)

N

V(N)

N

V(N)

2,595,688
2,839,645
3,346,199
4,623,131
4,652,724
17,407,519
35,464,906

171
178
180
174
181
182
190

1,236,572
975,193
1,264,017
2,143,677
1,958,581
7,644,807
15,222,847

107
120
130
132
141
152
155

4,444,897
4,310,230
5,233,293
8,138,270
7,888,834
29,837,539
59,853,063

606
651
663
660
686
710
726

Table 2: Basic data of our corpora 2

3. Methods
After we observe the basic characteristics, we make a text-feature matrix, whose rows represent
the texts per month and columns represent features (relative frequencies of each function word
to the sum of all the function words). Then we apply principal component analysis and random
forests.
First, we apply principal component analysis with the covariance matrix of the features. Principal
component analysis enables us to view as a scatter plot and to clarify the factors classifying the
texts as the principal component 8.

8different forms of an auxiliary verb have the same meaning in Japanese.
There are other methods of exploratory data analyses, e.g., factor analysis, correspondence analysis, or
multidimensional scaling. Even though there are no special rules for deciding which method is the best for

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EXPLORATORY ANALYSIS OF STYLISTIC CHARACTERISTICS IN JAPANESE Q&A COMMUNITIESNext we apply random forests proposed by Breiman (2001). We replicated the original data
matrix Mi,j 1000 times with replacement, and extracted random subsets of variables from each
replicated data. We constructed an unpruned decision tree for each sample by using the Gini
index. We constructed a new classifier by conducting a majority vote of the set of trees. Twothirds of the bootstrap samples were used for constructing the model and the other third wereleft for testing the model (out-of-bag test).
We calculated the variable importance using the following formula (Breiman, 2001):

Coob: number of votes cast for the correct class in the out-of-bag data
Cper: number of votes cast for the correct class when m variables are randomly permuted in theout-of-bag data
s.e.: standard error.
The mean value of subtractions for all trees formulated above represents the variable importancefor a permuted variable. It represents the degree to which a class loses its specific character
when one type of morpheme changes into another type of morpheme. This method calculates
important variables directly contributing to the classification in the experiment; thus, it suits forour purpose best (Suzuki, 2009).
We used the macro average of F1 values for evaluating the results. Random forests uses random
numbers in the experiments; thus, we performed the experiments 100 times and calculated themean F1 values for these 100 experiments (Jin and Murakami, 2007).

4. Results and discussion
4.1. Basic Observation
Tab. 1 lists the results of the basic observation, number of submissions, and number of tokens
for a submission (mean, standard deviations, coefficient of variation), for six categories. The
results show that on average LH has a larger number of submissions and is longer in length thanPC; BA is as long as Q; NA is shorter than others, and NA has larger variances than others.
Tab. 2 lists the respective numbers of tokens N and types (V(N)) for each part-of-speech andall the function words in six categories. These results show that BA has a larger N than Q interms of conjunctions and particles and a smaller N in functional nouns and auxiliary verbs. The
tendency of N is different between PC and LH adnominals. NA has larger N and V(N) comparedwith Q and BA. These basic characteristics show the differences between PC/LH and Q/BA/
NA, which we will discuss in Section 4.3 in detail.
4.2. Principal component analysis
We carried out a principal component analysis using the covariance matrix of the features 9.
Fig. 1 represents the scatter plot showing the first two principal components 10. The proportion

910

respective data, we selected PCA because its results tend to be unambiguous, and thus it should be applied first(Jin, 2007).
We also carried out the method using the correlation coefficient matrix, and found no significant differences
between results.
Texts are indicated by combinations of subject categories (PC/LH) and types (Q/BA/NA).

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of variance accounted for by the first principal component was 56.17%, and the cumulative
proportion of variances accounted for by the first two principal components was 93.37%. The
first principal component mainly represents the axis of subject categories, because the texts in
LH fell to the left side of the scatter plot and those in PC to the right side. The second principal
component mainly represents the axis of questions/answers, because questions fell on the upperside, and answers on the lower side. Best answers and normal answers were clearly distinguishedregarding PC, but not regarding LH. These results show that the difference between questions
and answers is rather large, and there is still a difference between best and normal answers
regarding writing styles. Three texts in a rather isolated position in the scatter plot (Q_LH04,
Q_PC04, B_LH04, N_LH04) were submissions in April 2004, in which the total number oftokens (N) and number of submissions were small.

4.3. Feature selection by machine learning
We next carried out six-class classification experiments (Q_LH, Q_PC, B_LH, B_PC, N_LH,
N_PC) by using random forests using the text-feature matrix of function words. Precision,
recall rate, and F1 value in the experiment was 97.06, 96.80, and 96.79 respectively.
Tab. 3 show the top 20 variables in the classification experiments using all the function words
with their parts-of-speech, variable importance (VIacu), and the notation that these variables
were frequently used (H) or infrequently used (L) in the category in comparison with mean ofother categories. These variables significantly contributed to the classification, and, thus, they
represent class-specific function words. We shall discuss the results by conducting qualitative
analyses. In this way, we will find many interesting issues that should be subjects of further,hypothesis-deductive research.
4.4. Comparison of subject categories regarding questions
Both categories include the particle ‘ka’ as a frequent expression. This is a typical expression
for making questions and a salient character of the question type. LH includes many pronouns
like ‘watashi’ (I; rank1), ‘kare’ (he; 2), ‘boku’ (I; 11), and ‘kanojo’ (she; 16), and adnominal
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EXPLORATORY ANALYSIS OF STYLISTIC CHARACTERISTICS IN JAPANESE Q&A COMMUNITIES‘konna’ (such; 6) and noun-affix ‘mitai’ (like; 5) as frequent expressions; these rarely appearedin the PC category. These results represent the characteristic of the LH category wherein people
tend to ask questions after they explain episode about themselves. The frequent expressions in
the PC category includes the particle-conjunctive ‘ga’ (-(subjective); 7) and noun-affix ‘no’(of; 4). These results represent the characteristic that people simply ask about people what theywould like to know.

Table 3: Top twenty variables with high variable importance (VIaci ) for respective classes

4.5. Comparison of best and normal answers
Both answers in the LH category include particle-conjunctive ‘tari’ (- (paralel), 6) as a frequent
expression, while it does not appear in the PC answers. ‘tari’ is a parallellization expression
used as ‘... sitari, ... sitari’. This result represents the characteristic of the LH category whereby
people tend to reply with several propositions, instead of the one specific solution, as is usually
expected in the PC category. BA in the LH category includes ‘anata’ (you; 18) as a frequent
expression, whereas NA in the LH category includes ‘anta’ (you; 14); the latter is a unpolite
version of the former in Japanese. This result implies that answers with polite expressions arelikely to be selected as a best answer. BA in PC includes the particle-adnominalizer ‘no’ (of; 2),particle-case ‘wo’ (-(objective), 3), and particle-case ‘ni’ (on; 4) as frequent expressions, while
NA in PC includes the particle-adverbial ‘ja’ (-(conversational), 3), particle-adverbial ‘nante’(-(conversational), 2), particle-final ‘ne’ (-(conversational), 7) and particle-final ‘yo’(-(conversational), 14). The former results imply that BA in PC is a writing style like that found in
instructional manuals containing specific explanations with clear and pertinence expressions (‘... wo
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TAKAFUMI SUZUKI, SHUNTARO KAWAMURA, AKIKO AIZAWA����361... ni ...’), while the latter results mean that NA in PC is a conversational style because all of thelatter expressions are chatty ones. The results show that regarding the PC category, polite and ‘manuallike’ submissions are likely to be selected as the best answers than conversational submissions.

5. Conclusion
This study was an exploratory analysis of the stylistic characteristics of text submitted toJapanese Q&A communities. After observing the basic characteristics of the submissions, weapplied principal component analysis and random forests to the text-feature matrix using therelative frequencies of function words. The results clearly show the stylistic characteristics
of questions, best answers, and normal answers regarding the two categories of ‘personal
computers and peripheral devices’ and ‘love and human relations advice’. This study provided
basic, but very important findings on how people differ in their communication styles regarding
subject categories and on how people select communication styles online.
Our findings will be the foundation of various research. First, we will develop methods todistinguish the best answers from normal answers, by extending the findings of this study.
Second, we will make a predictive transform or automatic paraphrase system focusing on thestyles that we identified. Third, we will investigate the changing language usage of Japanese bycomparing texts of the submissions against balanced corpora.

Acknowledgements
We were supported by Grant-in-Aid for Scientific Research 21800087 for Young Scientists(Start-up) from the Ministry of Education, Culture, Sports, Science and Technology, Japan, anda Mitsubishi Grant, 2008-2010. We used the Yahoo! Chiebukuro data provided by Yahoo Japan
Corporation to the National Institute of Informatics. We would like to express our gratitude forthe support. An earlier version of this study was presented at the 53th Annual Meeting of The
Mathematical Linguistic Society of Japan at the Tokyo Woman’s Christian University. We
would like to thank the participants of that meeting for their helpful comments.

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