=Paper=
{{Paper
|id=Vol-2696/paper_239
|storemode=property
|title=A Deep Learning Model to Predict Gender, Age and Occupation of the Celebrities based on Tweets Followers
|pdfUrl=https://ceur-ws.org/Vol-2696/paper_239.pdf
|volume=Vol-2696
|authors=Roobaea Alroobaea,Ahmed H. Almulihi,Fahd S. Alharithi,Seifeddine Mechti,Moez Krichen,Lamia Hadrich Belguith
|dblpUrl=https://dblp.org/rec/conf/clef/AlroobaeaAAMKB20
}}
==A Deep Learning Model to Predict Gender, Age and Occupation of the Celebrities based on Tweets Followers==
https://ceur-ws.org/Vol-2696/paper_239.pdf
A Deep learning Model to predict gender, age and
occupation of the celebrities based on tweets
followers
Notebook for PAN at CLEF 2020
Roobaea Alroobaea1, Ahmed H. Almulihi1, Fahd S. Alharithi2, Seifeddine Mechti2,
Moez Krichen3, Lamia Hadrich Belguith2
1 College of Computers and Information Technology, Taif University, Saudi Arabia
2 The laboratory on Development and Control of Distributed Applications (ReDCAD)
3 ANLP Group, MIRACL Laboratory, University of Sfax, 3018,Sfax Tunisia
r.robai@tu.edu.sa, a.almulihi@tu.edu.sa,
f.alshalawi@tu.edu.sa,mechtiseif@gmail.com, moez.krichen@redcad.org,l.belguith@gmail.com
Abstract. This paper present the methods are used for detection of celebrity
profiles on Twitter when participating in PAN @ CLEF 2020. We have tried to
predict the age, gender and occupation of celebrities based on their tweets
followers. Our method is based on the use of deep learning techniques to
discriminate between authors. The results obtained by the team ''TUKSA20'' are
encouraging, indeed we obtained the 1st rank for the prediction of the gender, the
second for the prediction of the occupation, and for the age the results obtained
are less good.
1 Introduction
Author profiling is the study which consists in recognizing certain dimensions of the
author's profile based on the stylistic features of their writings [Maharjan et al., 2014].
The dimensions targeted by profiling particularly concern the demographic and socio-
cultural aspects of the author, such as his age, sex, personality, mother tongue, native
region, and recently the occupation [Alroobaea, 2020; Wiegmann et al.,2019].
[Koppel, 2002; Argamon et al., 2009; Pennebaker, 2011].
Recently, the prediction of the author’s occupation has been the focus of work on the
author’s profile detection. The question is whether the author is a politician, footballer,
artist, singer, scientist, etc. predicting occupation has an important role in many domains
such as websites bidding and forensic linguistics.
The Celebrity Profiling task 2020 is to develop a piece of software which predicts three
demographics of a celebrity from the texts of their followers: occupation, age, and
gender.
In this paper, we try to implement a system for detecting the age, gender and occupation
of celebrities using deep learning techniques. The remainder of this paper is organized
as follows; Section 2 presents a brief state of the art of author profile prediction methods.
Section 3 introduces our method used to discriminate between authors. Section 4
presents the experiments as well as the evaluations. Finally, the summarization with
conclusion will be mentioned.
Copyright c 2020 for this paper by its authors. Use permitted
under Creative Commons License Attribution 4.0 International
(CC BY 4.0). CLEF 2020, 22-25 September 2020, Thessaloniki,
Greece.
2 Related Work
The study of [Wiegmann et al., 2019] distinguished two types of attributes: stylistic
attributes (style-based features) and attributes based on content (content features).
Stylistic features
In determining the age or gender of a writer, it is important to consider functional words,
such as prepositions, pronouns and determiners. These attributes have been shown to be
effective in detecting an author’s profile. [Cruz et al., 2013]. In other work, the authors
use the frequency of punctuation marks, the frequency of capital letters and citations
[Aleman et al., 2013]. Similarly, HTML attributes, such as image URLs or a web page
link, have been used by [Sapkota et al., 2013]. In the work of [Patra et al., 2013] the
authors are based on specific terms (foreign words) for the distinction between the
authors. Similarly, other authors use a calculation of the frequency of emoticons as a
discriminating attribute for the prediction of the authors [Irazu et al., 2013].
Content features
In addition to the style used, the occurrence of the themes used can differentiate
several age categories. [Argamon et al., 2009].
The age of groups generally represents adolescents, young people and adults.
Adolescents were grouped in the '10s' category presenting individuals between 13 and
17 years of age, young people in the '20s' category representing those between 23 and
27 years of age and finally adults in the '30s' category representing those aged between
33 and 47 years [Schler et al., 2006].
The work carried out by Koppel et al. have shown that at the gender level. Men prefer
to categorize things using more determinants (le / la, ce / ce, un / une, etc.) and quantifiers
(two, more, few, etc.). On the other hand, women frequently use personal pronouns in
their writings (I, you, me, etc.) [Koppel et al., 2003].
Argamon et al. (2009) have done a study on the British national corpus using the
elements of speech features. They obtained an 80% prediction for the determination of
the genus [Argamon et al., 2009]. For their part, [Nguyen et al., 2013] have tried to
predict age in the conversations of Dutch Twitter users. They used traits from the
language model combined with parts of speech. 74% of the discussions were well
classified. They obtained an average margin of error between 4.1 and 6.8 years for age
prediction. Similarly, in [Zhang and Zhang, 2010], the authors worked on blog segments
using word-type features, punctuations, average words per sentence, length of sentences,
part of speech and the rate of prediction of the gender which amounts to 72%. Peersman
et al. (2011) used a Netlog corpus by experimenting with the 2-gram, and 3-gram
unigrams. The average obtained for the prediction of age and gender is 88% [Peersman
et al., 2011].
In [Gaustad et al., 2007], the authors are interested in the automatic classification of
mails. They obtained an accuracy rate of 81% for gender and 72% for age. We cite
among others, the work of [Kose et al., 2007]; [Hariharan et al, 2011] which have shown
promising results in detecting the author’s gender in instant discussions. For their part,
Maharjan et al. have developed a system based entirely on the Map Reduce technique
for predicting age and gender. This technique is a parallel programming model allowing
the processing of a very large mass of data (Big data). These real-time programs are
executed on partitions (clusters) and are automatically parallelized [Maharjan et al.,
2014]. This system which employs a large number of functionalities is capable of
performing the prediction task in a fraction of time with good details. This approach
demonstrates that the use of these systems can be a perfect solution for data quantity
problems and / or a large number of attributes.
The objective of the study by Rangel et al. (2016) was to show how people use language
to express their emotions and how it can help them identifying their age and gender.
They used a graphical approach called "EmoGraph" which allows the text to be
represented by a graph of parts of speech labeled by emotions and themes. The authors
showed results comparable to those of the best systems presented at the PAN @ CLEF
2013 evaluation conference [Rangel et al., 2016].
3 Methodology
3.1 Preprocessing
Plain text should be filtered to remove noisy data, such as HTML tags and urls. This is
because the presence of this noisy data could affect and reduce the accuracy of the entire
analysis. [Derczynski et al., 2013] pointed out that ''twitter text is difficult to part-of-
speech tag: it is noisy, with linguistic errors and idiosyncratic style''. Thus, preprocessing
stage is important step. After that the cleaned data is then stored in a database.
3.2 text analysis
In this step, it is a matter of carrying out linguistic and statistical processing to find the
useful information allowing to subsequently achieve the best classification of
documents. To achieve our objective, we calculated the number of occurrences of all the
words that occurred in the corpus and thus obtained a list of words sorted by frequency.
Note that the text analysis step is repeated for each age group and for each gender. Then
we tried to gather these terms into thematic classes by adopting a semi-automatic
annotation which follows the following two steps:
-Automatic procedure: During this annotation, we based ourselves on the automatic
search of thematic classes for the attributes based on the content and of syntactic classes
for the attributes based on the style. Furthermore, the definition of thematic classes is
based on the notion of a universe of discourse. Indeed, all the terms presenting a semantic
relation are gathered around a centroid which bears the name of this class. In this
annotation, we use the Wordnet-Similarity tool to extract the semantic distances between
terms. We use synonymy, hypernymy and hypomimy relationships to detect
generalization and specification. At the end of this step all the terms having a semantic
distance with the “synset” belong to the same class. It should be noted that the stylistic
classes are obtained using the morpho-syntactic analyzer in order to be able to
differentiate prepositions, verbs, pronouns, etc.
- Manual procedure: Since the automatic annotation does not allow assigning all the
terms found in a class, we have had recourse to the manual annotation which is inspired
by the thematic classification proposed by [Argamon et al., 2009]. It consists of
manually grouping the most frequent terms in the same class. This operation is
performed by three different linguists.
3.3 Features extraction
Recent methods differentiate two main categories of features that can be used to
predict the profile of the author: Stylistic features and those based on content
[Wiegmann, 2019] .In order to measure the relevance of a term in a given document
in the corpus, we used the TF * IDF measure which is calculated as follows:
[Buckley et al., 1996]
1.1
With:
d: documents,
W: the word,
D: one document є D,
fw,d : number of occurrence of w in d,
|D|: size of the corpora,
fw, D : number of documents.
After forming the classes, the trait selection is used to elucidate which ones allow
better discrimination. This step is essential since the initial calculation of the frequency
of terms in the corpus does not provide information on the ability of the classes found to
discriminate between the different age / gender categories. In addition, the selection of
attributes is a very important factor which eliminates redundant attributes and which
results in improved classification performance.
It should be noted that the selection of attributes can be done in two ways. The first is an
"a priori" selection which allows you to filter the attributes before starting the
classification step. In this case, it is a question of employing filtering algorithms making
it possible to find, for example, the best K most discriminating attributes. As for the
second, it is an "a posteriori" selection which exploits the classification process: each
time it is a question of using a set of attributes for learning and of refining the results as
and when. This iterative process stops when the classification result is optimal.
Therefore, the attributes allowing to obtain the best results are retained as the most
discriminating attributes.
3.4 Classification
Once the attributes have been defined, the classification of the texts is proceed. It is
started with the construction of the two learning matrices relating to the gender and age
dimension. However, we have opted to enrich our base of thematic classes. The idea is
that each test corpus can contain terms semantically linked to existing terms in the
learning corpus. Therefore, expanding these terms is a step towards improving
relevance.
It should be noted that the enrichment of the base of thematic terms is defined as being
the process of completing the original terms of the thematic classification proposed by
associating them with semantically similar terms. It is one of the solutions envisaged to
enrich the proposed classes. Similar to the text analysis stage, in this enrichment process,
we have based ourselves on the exploitation of semantic links like synonymy, hyponymy
or hypernymy for the enrichment process.
5 Experimentations and Results
The LSTM deep learning model is presented in figure 1:
Fig 1: Architecture of the LSTM deep learning Model
In the evaluation process, Submissions are judged by a combined metric cRank,
which is the harmonic mean of each label's metric.
All features are evaluated by their respective F1. Precision and recall of birth-year are
calculated leniently. If a prediction is within an m-window of the truth, it is counted as
correct:
The window size m is based on the birth-year and increases linearly from about 3
years for 1999 to about 9 years for 1940.
After the evaluation process our team ''TUKSA20" obtain third rank in term of
CRANK and first rank for the prediction of the gender with 0.69. For the occupation we
obtain second Rank VS Hodge20.
TEAM TEST-DATASET
CRANK AGE GENDER OCCUPATION
Baseline-ngram-celebrity- 0.631 0.500 0.753 0.700
tweets
hodge20 0.577 0.432 0.681 0.707
koloski20 0.521 0.407 0.616 0.597
TUKSA20 0.477 0.315 0.696 0.598
Baseline-ngram-follower- 0.469 0.362 0.584 0.521
tweets
Random 0.333 0.333 0.500 0.250
Table 1. Official results of the PAN@CLEF celebrities profiling Task
6 Conclusion
This paper presents our method to predict gender, age and occupation of the
celebrities based on tweets followers. For the prediction of gender and occupation, good
results were obtain based on LSTM (Long Short term memory) architecture. Indeed,
TUKSA20 ranked the first for the gender prediction. However, the age prediction results
were less good because of the unbalanced data. As a perspective, other deep learning
models need to be test such as CNN (Convolutional Neural Network) , GRu ( Gated
recurrent Units) and GAN ( Adversarial Neural network).
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