Author profiling of Vietnamese forum posts - An investigation on content-based features
VNU Journal of Science: Comp. Science & Com. Eng., Vol. 33, No. 1 (2017) 37-46
Author Profiling of Vietnamese Forum Posts - An
Investigation on Content-based Features
Duong Tran Duc1,*, Pham Bao Son2, Tan Hanh1
1Posts and Telecommunications Institute of Technology, Hanoi, Vietnam
2VNU University of Engineering and Technology
Abstract
In this paper, we investigate the author profiling task for Vietnamese forum posts to predict demographic
attributes, such as gender, age, occupation, and location of the author. Although we conducted the experiments
on different types of features, including style-based and content-based features, we focused more on analyzing
the effects of content-based features. We used machine learning approaches to perform classification tasks on
datasets we collected from popular forums in Vietnamese. The results show that these kinds of features work
well on such a kind of short and free style messages as forum posts, in which, content-based features achieved
much better results than style-based features.
Received 28 June 2016; Revised 10 December 2016 & 08 February 2017; Accepted 18 February 2017
Keywords: Author profiling, machine learning, content-based features.
1. Introduction*
people do not provide their personal
information or input the incorrect/unclear data.
As a result, the task of automatically
classifying the author’s properties such as
gender, age, location, occupation, etc. becomes
important and essential. Applications of this
task can be in commercial field, in which
providers can know which types of users like or
do not like their products/services (for target
marketing and product development). For the
social research domain, researchers also want to
know the profile of people who have a specific
opinion about some social issues (when doing a
social survey). It can also be used to support the
court, in term of identifying if a text was
created by a criminal or not [1].
The rapid growth of World Wide Web has
created a lot of online channels for people to
communicate, such as email, blogs, social
networks, etc. However, online forum is still
one of the most popular channels for people to
share the opinions and discuss about the topics
which are interested in common. Forum posts
created by users can be considered as informal
and personal writings. Authors of these posts
can indicate their profiles for other people to
view as a function of forum. But not many
users reveal their personal information, because
of information privacy issues on the online
systems. Moreover, personal information of
users is not mandatory to input when they
register as a user of forums. Therefore, most of
Profiling the author of forum posts is also a
challenging task in comparison to doing this on
other formal types of text such as article, novel,
or even the other types of online texts such as
blog posts or emails. Forum posts are often
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38
short and written in free style, which may
contain grammar errors or informal sentence
structures.
analysis has moved the focus to this type of
text, such as email, blog posts, forum posts
[1, 7, 24].
Although most of previous works in author
profiling were conducted on online texts (blog
posts, emails), there are a litter works on more
informal style of texts such as forum posts.
These works also focused on the popular
languages such as English, Dutch, Chinese,
Greek, etc. [1, 4, 16, 23, 26]. As far as we have
known, there is only one work on author
profiling conducted in Vietnamese, but on blogs
and used style-based features only [6]. In this
work, we investigate the use of both style-based
and content-based features for author profiling
of Vietnamese forum posts, in which we report
a deeper analysis on content-based features.
This work is also an extension version of our
paper on author profiling which presented at
ACIIDS’16 [8]. In this paper, we investigated
further about the content-based features, such as
the best number of content-based features for
each trait (which yields the highest result), the
list of the most important features for each trait
with their weights and provide some analysis
about them. In addition, we also improve the
prediction results on some traits by applying the
Grid Search algorithm to select the best
parameters for SVM algorithm.
According to Zheng et al. [26], the
authorship analysis studies can be classified
into three major fields, including authorship
attribution, authorship profiling, and similarity
detection.
Authorship attribution is the task of
determining if a text is likely written by a
particular author or not. It also is the technique
to identify which one from a set of infinite
authors is the real author of a disputed
document. Therefore, it is also called authorship
identification. The first study in this field dates
back to 19th century when Mendenhall (1887)
[14] investigated the Shakespeare’s plays. But
the work which was considered the most
thorough study in this field was conducted by
Mosteller and Wallace (1964) [15] when they
analyzed the authorship of FederalList Papers.
From that point, a number of works have been
conducted by various researchers, including
[2, 5, 7, 11, 21, 23, 26].
Authorship profiling, also known as
authorship characterization, detects the
characteristics of an author (e.g. gender, age,
educational background, etc.) by analyzing the
texts created by him/her. This technique is
different from the former in that it is often used
to examine the anonymous text, which is
created by an unknown author, and generates
the profile of the author of that text. For this
reason, the author profiling task is often
conducted on the online documents rather than
literary texts. Therefore, this field is only more
concerned by researchers from the late of
1990s, when more and more online documents
are created by Internet’s users. The most typical
studies in this fields are from [2, 3, 4, 6, 9, 10,
11, 12, 16, 17, 18, 20, 22, 24].
The organization of the paper is as follows.
In section 2, we present the related work on the
author analysis problem. Section 3 describes the
methods and the system. Section 4 presents the
result and discussion. In section 5, we draw a
conclusion and future work.
2. Related work
The problem of authorship analysis has
been studied for decades, mostly on English
and some other languages (Dutch, French,
Greek, Arabia etc.). In the early stage, it was
often conducted on the long and formal
documents such as article or novel. However,
since 1990s, when the WWW grew and created
a large amount of online text, the task of author
Similarity detection, on the other hand,
doesn’t focus on determining the author or
his/her characteristics, but analyzes two or more
documents to find out if they are all created by
the same author or not. This technique is also
used to verify if a piece of text is written by the
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39
author himself/herself or copied from the
product of other authors. This task is mostly
used for plagiarism detection. Some of the most
convincing studies in this field were conducted
by [2, 5, 7] and [11].
[11]) or selecting from corpus by frequency or
by other feature selection methods [4].
Also the investigation of Zheng et al. [25]
showed that, in early studies most authorship
analytical techniques were statistical methods,
in which the probability distribution of word
usage in the texts of each author was examined.
Although these methods achieved good results
in authorship analysis, there are still some
limitations, such as the ability to deal with
multiple features or the stability over multiple
domains.
To overcome those limitations, the
extensive use of machine learning techniques
has been investigated. Fortunately, the advent
of powerful computers allows researchers to
conduct the experiments on complicated
machine learning algorithms, in which Support
Vector Machine (SVM) shows the better results
in many cases ([1, 2, 5, 6, 7, 11, 12, 18, 20, 22,
26]). Some other machine learning algorithms
also have been examined and achieved good
results, including Bayesian Network, Neural
Networks, Decision Tree ([4, 11, 22, 25]). In
general, machine learning methods have
advantages over statistical methods because
they can handle the large features sets and the
experiments also shown that they achieved the
better results.
Regarding the process of authorship
analysis, there are two main issues that may
significantly affect the performance, namely
features set and analytical techniques [26].
Features set can be considered as a way to
represent a document in term of writing style.
With a chosen features set, a document can be
represented as a features vector in which entries
represent the frequency of each feature in the
text [12]. Although various types of features
have been examined, there is no features set
that is the best to all the cases. According to
Argamon et al. [4], there are two types of
features that often can be used for authorship
profiling: Style-based features and content-
based features.
Style-based features can be grouped into
three types, including lexical, syntactic, and
structural features. Lexical features are used to
measure the habit of using characters and words
in the text. The commonly used features in this
kind consist of the number of characters, word,
frequency of each kind of characters, frequency
of each kind of words, word length, sentence
length [7], and also the frequency of individual
alphabets, special characters, and vocabulary
richness [11]. Syntactic features include the use
of punctuations, part-of-speeches, and function
words. Function words feature is the interesting
kind of features, which is examined in a number
of studies and yielded very good results ([11,
22, 26]). The set of function words used is also
varying, from 122 to 650 words. Structural
features show how the author organizes his/her
documents (sentences, paragraphs, etc.) or other
special structures such as greetings or
signatures ([5, 11]).
This paper addresses the problem of author
profiling for forum posts, which are in type of
online text and written in free-style with short
length. For this kind texts, it may be difficult to
capture the pure style of authors and using
content words as discriminating features could
improve the author profiling results.
3. System description
3.1. System overview
In this work, we built a system which can
take sample texts from web crawlers, then used
text and linguistic processing components to
extract features to create the data sets for the
purpose of training the classifier. The classifier
Content-based features are often specific
words or special content which are used more
frequent in that domain than in other domains
[25. These words can be chosen by correlating
the meaning of words with the domain ([2],
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40
then can be used to predict the profile of the
people) and age periods are not continuous
because distinguishing two contiguous ages is
almost impossible. With the occupation trait,
we tried to identify three occupations which are
the most popular (business, sale, administration
/technical, technology/education, healthcare).
Linguistic processing is the task of
tokenizing the text into sentences or word and
the tagging for part-of-speeches. These tasks
are important for extracting the word and
syntactic features in the next step. In this work,
we used existing tools from [19].
Lastly, the value of each feature is
calculated to form a feature vector and saved to
training datasets.
In the next sections, we describe the
features and techniques which were used for
classification in detail.
author of an anonymous forum post. Fig.1.
shows the overall structure of the system.
In the data processing step, data is selected,
cleaned and grouped by author profiles. Only
posts with length from 50 to 300 words (250 to
1500 characters) were used. We also applied
both automatic and manual text processing
activities such as eliminating the spam texts,
abnormalities, updating training labels, etc.
Unlike the gender and location trait, which can
be divided into two groups (male/female,
north/south), the other traits are grouped by
more than 2 classes. For age trait, we
categorized our data into 3 subclasses (less than
22/24-27/more than 32). Age is categorized
according to the life stages of a person (students
or pupils/young working adults/middle-age
G
Data crawling
Raw data
Data preprocessing
Features extraction
Feature vectors
Feature vectors storage
Training
Prediction model
Feature container
Fig 1. Overall architecture of the system.
3.2. Features
Style-based features include character-based,
word-based, structural, and syntactic features.
In this work, we used common style-based
features which were used from the previous
work in other languages, such as the number of
characters/words, ratio of each type, etc. We
As mentioned earlier, various features can
be used to identify the characteristics of an
author. In this work, we used both style-based
and content-based features.
D.T. Duc et al. / VNU Journal of Science: Comp. Science & Com. Eng., Vol. 33, No. 1 (2017) 37-46
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also chose 212 Vietnamese function words,
which have little lexical meaning.
Part-of-speech tags include 18 word types, such
as noun, verb, preposition, etc.
result than other classifiers. Although SVM is a
binary classifier, it can handle the multiclass
problem (as in case of age and occupation) by
building the classifiers which distinguish
between every pairs of classes and then using
the voting strategy to determine the instance
classification.
In addition to SVM, we also used
Information Gain method for feature selection
and Grid Search for parameter tuning to select
the best features and parameters. Information
Gain is one of the most popular feature
selection methods, which attempts to measure
the significance of each feature in
distinguishing between classes [24]. This
method was tested on various previous works
and yielded the good result.
Content-based features used in our work
were chosen from the corpus, which are the
words that can discriminate best between
classes of each trait. Firstly, these words were
selected based on the frequency of them in the
corpus (separately by classes of each trait).
Then the Information Gain, a feature selection
method, was applied to select the best features.
For gender trait, we selected 2000 words which
were used most frequently by male/female
separately. After eliminating the identical words
and applied the Information Gain method, we
chose the words which have highest
significance. Using the similar process, we
chose the most significant words to use as
content-based features for discriminating the
age, occupation, and location traits.
We also experimented on the subsets of
features (Style-based, Content-based, All) to
investigate the performance of each type.
All of these features are extracted from the
text and store in a numeric vector. For features
which need some kinds of linguistic processing
activities, such as the word segmentation or the
part-of-speech tagging, we used existing tools
available for Vietnamese. Extracted features are
stored in the features containers, then are sent to
classifiers for training purposes and prediction
models are built for classifying the new data.
We also conducted experiments on subsets
of features, including Style-based features,
content-based features, and all features for
analysis of performance of each type.
4. Experiments
4.1. Data
There are a number of Vietnamese forums
which we can collect the data. However, each
of them often serves for a specific type of user
only (e.g. for ladies or gentlemen) or for a
specific subject of interest such as technology,
automobile etc. Therefore, we selected three
forums to collect data to ensure that the data
collected will cover a wide range of users and
subjects.
3.3. Learning methods
A forum for girls and ladies to discuss about the
variety of subjects in life and work.
In this work, we used Support Vector
Machine (SVM) as the learning method to build
the classifiers for input messages. Support
Vector Machine is a learning method having an
advantage that it does not require a reduction in
the number of features to avoid the problem of
over-fitting. This property is very useful when
dealing with large dimensions as encountered in
the area of text categorization [5]. SVM has
been used in many previous works in author
analysis and in most case achieved the better
for mostly the men to exchange about issues of
automobile and related subjects.
for young people to exchange the topics about
technological devices and interests.
Users of these forums can indicate the
personal information such as name, age, gender,
interest, job etc. in their profiles. However,
D.T. Duc et al. / VNU Journal of Science: Comp. Science & Com. Eng., Vol. 33, No. 1 (2017) 37-46
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none of them is the explicit field in the user’s
parameters c and exponent, together with some
modifications in the feature extraction step, the
results improved noticeably compared with
results in [8], specially on age, location, and
occupation traits (e.g. the best parameters for
gender trait are c=3.0 and exponent=1.0). Table
2 shows the results of author profiling
experiments of 4 traits.
profile. Therefore, we collect only the data
which contain information about at least one
author trait.
After the last step, we obtained a collection
of 6.831 forum posts from 104 users (736.252
words in total), for which we also received at
least one of the information about age, gender,
location, occupation of the author of each post.
The length of each post is also restricted in the
range from 250 to 1500 characters to eliminate
the too long or too short posts (too long post
may contain the text copied from other
sources). The average lenth of posts in words is
107 (the short test post contains 50 words, the
longest post contains 300 words).
General evaluation. As the results shown
in Table 2, we can observe that content-based
features outperformed Style-based features.
Although content-based features are often
considered domain-specific and may be less
accurate when moving the other domains, the
results in this task are still promising. Firstly,
the data in corpus was collected from various
source, therefore it is not so domain-specific.
Secondly, even the results are domain-specific
to some extent, it is still useful when we
conduct the research or apply the results in that
domain. Besides, the results of Style-based
features are also good, especially for gender and
location. Generally, using content-based
features increases the accuracy from 7% to 8%,
but the improvement is more than 11% for the
location trait. Therefore, we may infer that
prediction of location is more sensitive on
content-based features than other traits. It is
reasonable because people from north and south
of Vietnam often use different local words in
casual communication.
Table 1. Corpus Statistic
Trait
Gender
Age
Total
posts
4.474
Class
Percent in
corpus
54%
Male
Female
< 22
24 to 27
> 32
North
46%
21%
27%
52%
3.017
3.960
Location
57%
South
43%
Occupation 3.453
Business,
Sale, Admin
Technique,
Technology
Education,
Healthcare
36%
31%
33%
Table 2. The results of author profiling experiments
4.2. Results and discussion
Feature
All
Gender
90.55
Age
70.70
Location
83.13
Occup-ation
61.04
We conducted experiments on 4 traits of
authors as mentioned earlier using the Weka1
toolkit. The results were verified through a 10-
fold cross validation process, in which the
training set is randomly partitioned into 10
equal size subsets and 9 subsets were used as
training data and the remaining subset is
retained for testing. This process is then
repeated 10 times with each of 10 subsets is
used exactly once as the validation data. Using
Grid Search for SVM on PolyKernel with two
Features
Style-
based
Content-
based
83.47
90.01
62.76
70.05
71.22
82.98
52.46
60.99
Number of content-based features. As
mentioned earlier, to reduce the complexity and
improve the accuracy of the model, we applied
a feature selection method to eliminate the
irrelevant features. We experimented the
classification with different number of content
words which were chosen by Information Gain
_______
1 http://www.cs.waikato.ac.nz/ml/weka/
D.T. Duc et al. / VNU Journal of Science: Comp. Science & Com. Eng., Vol. 33, No. 1 (2017) 37-46
43
method, ranging from 100 to 1000. Fig 2.
shows the best number of features for each trait.
The figure shows that the highest score of
gender prediction is achieved when using 600
content words. The best number of words for
age and location traits is 400 and the occupation
trait is 200. The reason for this is probably the
noise in occupation data and therefore, not
many words can be used to discriminate
between the classes of occupation. Table 3
shows some of the most important content
words with their weights for each trait (the
bigger absolute value of weight is, the more
important the feature is).
Fig 2. Prediction accuracy for different numbers of
content words.
Table 3. The top important content words for each trait
(a) Important words for gender prediction
Male Female
weight feature weight feature weight
feature
mục tiêu
dữ liệu
weight
feature
-1.35
-1.34
quy định
máy ảnh
điện tử
-1.18
cảm ơn
1.91
1.79
1.76
1.56
1.51
hồng
bếp
1.46
1.43
1.31
1.27
1.18
-1.09 khách sạn
doanh nghiệp -1.32
-1.07
cưới
bác sĩ
vải
sữa
kỹ thuật
xử lý
-1.31 triển khai -1.03
-1.26 kiểm tra -1.02
chia sẻ
áp lực
(b) Important words for age prediction
Middle
Younger
weight
Older
feature weight
feature
feature
nhu cầu
triệu
weight
-1.29
-1.20
-0.90
-0.74
-1.03
học hỏi
lịch sử
-1.50
-1.32
-1.25
-1.05
-0.80
xài
luật
1.24
1.11
0.66
0.62
0.58
nguyên do
hành động
thể thao
khắp nơi
lang thang
bỏ qua
quy định
chi phí
hỗ trợ
(c) Important words for location prediction
North South
weight weight feature
feature
buổi
đỗ
mạch
liệu
feature
rẽ
weight
-0.78
-0.73
feature
máy lạnh
coi
weight
1.09
1.04
1.00
1.00
0.91
-1.22
-1.18
-1.05
-1.00
-1.00
1.52
1.51
1.48
1.46
1.35
gởi
đậu
xài
uổng
dơ
quay
sinh
ảnh
-0.70
-0.65
-0.53
gạt
nhơn
quẹo
nộp
chịu khó
D.T. Duc et al. / VNU Journal of Science: Comp. Science & Com. Eng., Vol. 33, No. 1 (2017) 37-46
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(d) Important words for occupation prediction
Business/Sale/Admin
Technology/Technique
Education/Healthcare
feature
weight
-1.64
-1.62
-1.21
-0.88
-0.77
feature
phát triển
cấu hình
kết hợp
kỹ thuật
tài liệu
weight
1.68
1.60
1.53
1.30
1.20
feature
tâm lý
hình ảnh
xã hội
học
weight
1.61
1.58
1.43
1.13
1.09
lịch
cuộc
lang thang
đến nơi
cung cấp
từ thiện
H
The words in tables suggest that the men
when saying about the location trait, but the
occupation prediction is not so good. The main
reason is that occupation information is very
noisy and subtle. For example, a person who
studied about technical but then works as a sale
person is not an easy case when predict his/her
job. This needs to be investigated further in
later researches.
When comparing with the only previous
work on author profiling in Vietnamese by [6],
for the gender trait, we achieved the better
result (90.55% and 83.3%) when using
content-based features, and the same result
(83.47% and 83.3%) without content-based
features. It showed that our approach when
adding the content-based features has improved
the results significantly. The same evaluation can
be said when comparing the results of location
trait. But for other traits, our results are less
accurate, but it is understandable and still
promising, because our experiments were
conducted on a shorter and more informal type of
text than blog posts.
tend to discuss about work, technology,
regulation etc. while the women often talk
about life, health, pressure, and so on. Young
people like to discuss about learning, action,
etc. The middle age people talk about the needs,
travel, and the older people often exchange the
views on expenses, law, etc. There many local
words that the northern and southern people
often used differently from each other, but in
our corpus, we found some of them as in the
Table 3 (c). Table 3 (d) shows that the people
working in business, sale field often used words
related to schedule, appointments, travel, while
the people working in technology field like to
talk about development, machine, etc., and the
people which have jobs in education/healthcare
fields often discuss about the social, learning,
charity issues.
Comparison with previous works. In
comparison to the results of previous works,
although forum posts are shorter and noisier
than other types of online messages such as
blog posts or emails, but the results can be
considered as promising, especially for gender
and location traits. The accuracy of 90.55%
when predicting the gender is even better than
the results of most of previous works which
were conducted on blogs or emails (which had
base-line about 80%). The percentage of age
prediction (70.70%) is not as good as the results
conducted on blog posts or emails (which had
the base-line around 77% for blog posts), but
much better compared to the result of a research
on forum posts conducted by [16], which is
only 53%. The same evaluation can be used
5. Conclusion
In this study, we investigate the author
profiling task on
a
different language
(Vietnamese) and different type of text (forum
posts) than previous works. The results show
that it is feasible to classify authorial
characteristics of the informal online messages
as forum posts based on linguistic features, in
which using content-based features improved
the results significantly. We also have a
D.T. Duc et al. / VNU Journal of Science: Comp. Science & Com. Eng., Vol. 33, No. 1 (2017) 37-46
45
[5] Corney, M., DeVel, O., Anderson, A., Mohay,
G. Gender-preferential text mining of e-mail
discourse. In ACSAC’02: Proc. of the 18th
Annual Computer Security Applications
Conference, Washington, DC, pp : 21-27. (2002)
[6] Dang, P., Giang, T., Son, P. Author profiling for
Vietnamese blogs. International Conference on
Asian Language Processing (2009).
[7] De Vel, O., Anderson, A., Corney, M., Mohay,
G. M. Mining e-mail content for author
identification forensics. SIGMOD Record 30(4),
pp. 55-64 (2001).
thorough analysis on content-based features,
such as the best number of content words and
the list of important words for each trait.
Experiments conducted show the promising
results, although some aspects still need to be
improved such as the solutions for noisy
information in occupation trait or the result for
age prediction should be better and so on.
In future, this study can be expanded to
other domains, such as social networks or user
comments/product reviews. The data in these
domains is even shorter and noisier than forum
posts, so it is more challenging task. But the
results of such kind of works have promising
applications in commercial fields, such as
analyzing market trends or user behaviors
prediction etc.
[8] Duc, D.T., Son, P.B., Hanh, T. Using
Content-based Features for Author Profiling of
Vietnamese
Forum Posts.
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Recent
Developments in Intelligent Information and
Database Systems, pp. 287–296. Springer
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[9] Goswami, S., Sarkar, S., and Rustagi.M.
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Finin, Natalie S. Glance, Nicolas Nicolov, and
Belle L. Tseng, editors, ICWSM. The AAAI
Press (2009).
[10] Gressel, G., Hrudya, P., Surendran, K., Thara,
S., Aravind, A., Prabaharan, P. Ensemble
learning approach for author profiling, Notebook
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[12] Koppel, M., Argamon, S., Shimoni, A.R.
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We also have planned to investigate about
the use of more grammar-based features in this
kind of task. Vietnamese has many interesting
linguistic features such as tones, spells, and we
can exploit these features to improve the author
profiling results.
Acknowledgements
This work has been supported by Vietnam
National University, Hanoi (VNU), under
Project No. QG.16.91
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