=Paper=
{{Paper
|id=Vol-1179/CLEF2013wn-PAN-SantoshEt2013
|storemode=property
|title=Author Profiling: Predicting Age and Gender from Blogs Notebook for PAN at CLEF 2013
|pdfUrl=https://ceur-ws.org/Vol-1179/CLEF2013wn-PAN-SantoshEt2013.pdf
|volume=Vol-1179
|dblpUrl=https://dblp.org/rec/conf/clef/SantoshBSV13
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==Author Profiling: Predicting Age and Gender from Blogs Notebook for PAN at CLEF 2013==
https://ceur-ws.org/Vol-1179/CLEF2013wn-PAN-SantoshEt2013.pdf
Author Profiling: Predicting Age and Gender from Blogs
Notebook for PAN at CLEF 2013
K Santosh, Romil Bansal, Mihir Shekhar, and Vasudeva Varma
International Institute of Information Technology, Hyderabad
{santosh.kosgi, romil.bansal, mihir.shekhar}@research.iiit.ac.in, vv@iiit.ac.in
Abstract Author profiling is the task of determining age, gender, native language
or personality type of author by studying their sociolect aspect, that is, how lan-
guage is shared by people. In this paper, we propose a Machine Learning ap-
proach to determine unknown author’s age and gender. The approach uses three
types of features: content based, style based and topic based. We were able to
achieve an accuracy of 64.08%, 64.30% for age and 56.53%, 64.73% for gender
in English and Spanish respectively.
Keywords: Author Profiling, Topic Modelling, Text Categorization, Natural Lan-
guage Processing
1 Introduction
The problem of identifying the user’s profile from the text is always of importance as
it helps in various fields like forensics and marketing. For example, in marketing, a
manager might want to find the gender and age group of people who like or dislike their
products from the public reviews. The increasing accessibility of public blogs offers an
unprecedented opportunity to harvest information from texts authored by hundreds of
thousands of different authors. In this paper, we tried to exploit these public blogs to
find the relations between the author’s profile and the language style used by them. The
main idea behind this task is to analyse how everyday languages reflects basic social
and personality traits. The profiling dimensions we considered are age and gender.
2 Approach
2.1 The Corpus
We used the blog corpus provided by PAN 2013[1]. The corpus consisted of blogs
written in both English and Spanish and each blog is written by either male or female
and belongs to one of three age groups(10s: 13-17, 20s: 23-27 and 30s: 33-47). The
corpus is described in more details in Table 1.
EN ES
10s 20s 30s 10s 20s 30s
Male 8,600 42,900 66,800 1,250 21,300 15,400
Female 8,600 42,900 66,800 1,250 21,300 15,400
Table 1. Blogs Distribution for English and Spanish Dataset
2.2 Features and Experiments
Different people tend to write differently. These differences occur due to variations
in the topics of interest and style of writing like word choices and grammar rules. For
example, females tend to write more about wedding styles and male tends to write more
about technology and politics. Further females use more adverbs and adjectives while
writing compared to males[8]. We considered these differences in the writing styles
and content of male and female bloggers of different ages. Overall we considered three
different types of features that are useful for distinguishing between different categories;
they are: content based features, style based features and topic based features. These
features are described in details below.
Content Based Features Male and female authors tend to speak about different topics,
so they will use different words. Thus content based features are important to distin-
guish between male and female bloggers[9]. For example, a blog related to cricket is
more likely to be written by a male author rather than a female. A blog related to cricket
may contain words like cricket, no ball, wide, world cup, icc world cup etc. Thus the
occurrence of words like world cup, cricket will increase the chances of it being written
by male rather than female blogger and occurrence of words or phrases like my hus-
band, pink, boyfriend will increase the chances of it being written by female. The words
which are used more frequently by one of the classes when compared to other can be
used as features. We calculated the frequencies of different N-grams in the documents
written by a particular gender. Then, for every N-gram, we calculated the ratio of its fre-
quencies in the blogs written by male and female bloggers. We took the top k N-grams
(We used k as 50000 and 40000 for English and Spanish gender analysis respectively)
that differentiate males from females and females from males as features.
Similarly, teenagers tend to write more about their friends and mood swings, whereas
people of 20’s write more about college life and people of 30’s write more about mar-
riage, jobs and politics. Thus content based features are important to distinguish be-
tween bloggers belonging to different age groups. Again, the words with most skewed
ratios are used as features. We used k as 40000 for both English and Spanish age anal-
ysis.
Style Based Features Style based features includes N-grams of POS tags in docu-
ments, punctuation symbols and number of href links[2,9]. For each of these features
we calculated its frequency with which it appears in the corpus. We used their nor-
malised count for creating numerical vector. This was the only language dependent
feature.
Topic Based Features N-gram based approach models the top words used by both
males and females. But many times same words are used in different contexts. For
example, males usually use words like ’daily life’ to describe their work and whereas
females use ’daily life’ to describe their love or spiritual life. Males use ’dresses’ in
context with pants and coats whereas females use ’dresses’ with words like bridal wears
and gowns etc. Topic based features consider the fact that different categories of people
have different topic of interests. We tried to model these differences to predict age and
gender of the person. We ran LDA1 algorithm to find topics from the blog and created a
machine learning model based on the probability distribution of the blog over different
topics and the class it is in.
For extracting the topic based features we divided the training data created in ratio
60%and 40%. The 40% of the data is used to train the MaxEnt model to predict the
class based on the topic distribution of the blog. The rest 60% of the data we used for
extracting relevant topics from the blogs. The topics were extracted as follows.
Overall Topics We gave the complete 60% of the data to generate topics from the blogs.
The intuition was that the different category of people tends to write on completely dif-
ferent topics. So modelling the users based on the topics would tell us the class of the
people the author belongs. Using this approach we achieved 52.3%(using 200 topics2 )
accuracy for gender classification. We analysed the topics of the blogs that are getting
misclassified by method. We analysed that although few topics completely distinguish
between males and females but most of the topics are written by both males and fe-
males. For example, the topic corresponding to ’dresses and shopping’ was thought to
be written by mostly females but males were also blogging about the topic. This causes
the algorithm to find topics distribution vector that could distinguish between males and
females completely. Similar case was observed with the different age groups.
Individual Topics Even if males and females write on the same topic, the words or
context used by them to describe the topic is different. This could be seen from the
above example as males are talking about pants and coats in the blogs for topic ’dressing
and shopping’ whereas females are talking about bridal wear and dresses in the similar
topic. The method of ’Overall Topics’ classified both in the same topic, thus making the
topic noisy. So to improve the creation of topics, we trained the topics separately for
individual classes and predicted the distribution over all of them. This helps us to model
the context in which the topic was spoken about. Using this approach we obtained the
overall accuracy of 54% for gender classification. We created 200 topics for each gender
and 100 topics for each age group while creating the model.
Hybrid Topics The above method gave better results, but some of the overall topics
are good enough to distinguish between different classes. So we created feature vector
as probability distribution over both individual as well as overall topics. We took 200
topics from each gender and 100 topics from each age group along with 200 overall
topics. Using this approach, we obtained the overall accuracy of 54.8% accuracy for
gender classification.
1
https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation
2
We experimented using different number of topics and found 200 topics to perform the best.
2.3 Learning Methods
We used the decision tree of classifiers to predict the class. We divided our corpus into
three parts. We trained the ML algorithm using content based, style based and topic
based features separately using the first part. We tested these models on the second part
and the output is used to train the final decision tree classifier. The third part is used as
a testing set. The table 2 shows Machine learning methods used to build classifiers.
Feature Name Feature Description ML Algorithm Used ML Library Used
Content Based Features Ngrams SVM SVM light[5]
Style Based Features Ngrams of POS tags SVM SVM light
Topic Based Features LDA Topic Model MaxEnt Mallet[6]
Merged Features Scores of classes from Decision Tree Mallet
different models
Table 2. Features used while training the models.
3 Conclusion and Future Work
A good system for author profiling is required in various domains ranging from analysing
sensitive text for national security to commercially important data from various com-
ments and product reviews. In our approach, we tried to model the author’s profile
using the writing style and content of the blog. We have shown that best results were
acchieved when the context information is used along with the content and style of the
blog.
Future efforts can be put into inducing sentiment analysis to discover more differ-
ences in text written by authors representing different classes. With further develop-
ments, we can expect much better accuracy rates in identifying the author’s profile.
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