SENTIMENT MINING OF ARABIC TWITTER DATA by Soha Galalaldin Khider Ahmed A Thesis

106 Pages·2014·2.84 MB·English

SENTIMENT MINING OF ARABIC TWITTER DATA

by

Soha Galalaldin Khider Ahmed

A Thesis Presented to the Faculty of the
American University of Sharjah
College of Engineering
in Partial Fulfillment
of the Requirements
for the Degree of

Master of Science in
Computer Engineering

Sharjah, United Arab Emirates

January 2014
© 2014 Soha Galalaldin Khider Ahmed. All rights reserved.

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Approval Signatures

We, the undersigned, approve the Master’s Thesis of Soha Galalaldin Khider Ahmed.

Thesis Title: Sentiment Mining of Arabic Twitter Data

Signature Date of Signature
(dd/mm/yyyy)

___________________________ _______________
Dr. Michel Pasquier
Associate Professor, Department of Computer Science and Engineering
Thesis Advisor

___________________________ _______________
Dr. Ghassan Qaddah
Associate Professor, Department of Computer Science and Engineering
Thesis Co-Advisor

___________________________ _______________
Dr. Khaled El Fakih
Associate Professor, Department of Computer Science and Engineering
Thesis Committee Member

___________________________ _______________
Dr. Ashraf Elnagar
Professor, Department of Computer Science, University of Sharjah
Thesis Committee Member

___________________________ _______________
Dr. Assim Sagahyroon
Head
Department of Computer Science and Engineering

___________________________ _______________
Dr. Hany El-Kadi
Associate Dean
College of Engineering

___________________________ _______________
Dr. Leland Thomas Blank
Dean
College of Engineering

___________________________ _______________
Dr. Khaled Assaleh
Director of Graduate Studies

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Acknowledgments

Foremost, I would like to thank Allah for giving me health, patience and strength to
write this thesis and for all the graces He has granted me.

I would like to thank my supervisors Dr. Michel Pasquier and Dr. Ghassan
Qaddah for advising me during my graduate study years. Thank you very much for
your patience, guidance and encouragement. I learnt from you how to be a real
researcher and how to think differently. I would also like to thank all my professors
and friends at AUS specially my friend Kouloud Safi.

Finally, I dedicate this thesis to my family who has always supported me in
my studies and life. Without your love, care and patience, I would not have achieved
this. Special thanks go to my father for believing in me and supporting me in my life
and education. My father believed that education was the ticket to success. He
particularly wanted me to be a professor and I will try hard to make his and my dream
true. I am also grateful to my brothers, Mohamed, Yousif, Khider and Omer and my
dear sister Line and my fiancé Osman for their help and continuous support.

Special dedication of this thesis goes to the most beloved person: Mum. Thank
you for your patience, care and everything you have done to keep our family gathered
in peace and happiness. Thank you for giving us the love we need to survive in this
life. I will always love you Mum.

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To my beloved parents, brothers and sisters for their endless love and
support

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Abstract

Social networking services such as Facebook and Twitter and social media hosting
websites such as Flickr and YouTube have become increasingly popular in recent
years. One key factor to their attractiveness worldwide is that these sites and services
allow people to express and share their opinions, likes, and dislikes, freely and
openly. The opinions posted range from criticizing politicians to discussing football
matches, citing top news, appraising movies, and recommending new products and
services such as mobiles, restaurants, and software. This development has fueled a
new field known as sentiment analysis and opinion mining with the goal of extracting
people’s sentiment from text to assist customers in their purchase decisions and
vendors in enhancing their reputation. This emerging field has attracted a large
research interest, but most of the existing work focuses on English text. Hence, in this
thesis, we studied sentiment analysis of Arabic text retrieved from a well-known
social media site, namely Twitter. Specifically, we studied the topic of target-
dependent sentiment analysis of Arabic Twitter text, which has not been addressed in
Arabic language before. We developed a system that will acquire Arabic text from
Twitter and extract users’ opinions towards different topics and products. Key phases
of the system are as follows. In the Data Acquisition phase, we collected tweets from
Twitter related to specific topics. In the Tweet-Filtering phase, we reduced the noise
in the collected tweets data to facilitate the Annotation phase, in which we annotated
the collected tweets depending on the specified topic. In the Data Preprocessing
phase, we added tags, normalized the words used in tweets, and removed spam
tweets. In the Feature identification phase, we extracted stylistic, syntactic, and
semantic features, and selected those yielding better results using features selection
algorithms. In the Classification phase, the decision to annotate the tweets as negative,
positive, or neutral towards a specific topic was made using a trained machine-
learning algorithm. Results from different feature sets, classifiers, and datasets are
reported in terms of classification accuracy, Kappa statistic, and F-measure.
Search Terms: Arabic Social Media, Machine Learning, Semantic Features,
Sentiment Analysis, Stylistic Features, Syntactic Features, Text Preprocessing.

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Table of Contents

Approval Signatures....................................................................................................... 3
Acknowledgments.......................................................................................................... 4
Abstract .......................................................................................................................... 6
List of Figures .............................................................................................................. 11
List of Tables ............................................................................................................... 12
1. Introduction .......................................................................................................... 15
1.1 Background ................................................................................................... 15
1.2 Problem Statement ........................................................................................ 16
2. Literature Review ................................................................................................. 20
2.1 Basic Taxonomy ............................................................................................ 20
2.2 Sentiment Classification Tasks ..................................................................... 20
2.2.1 Data preprocessing. ................................................................................ 20
2.2.2 Class labeling. ........................................................................................ 22
2.2.3 Annotation granularity. .......................................................................... 23
2.2.4 Source and target identification. ............................................................ 23
2.3 Sentiment Classification Features ................................................................. 26
2.3.1 Syntactic features. .................................................................................. 26
2.3.2 Semantic features. .................................................................................. 26
2.3.3 Stylistic features. .................................................................................... 27
2.4 Sentiment Classification Techniques ............................................................ 28
2.4.1 Machine learning. .................................................................................. 28
2.4.2 Link analysis. ......................................................................................... 29
2.4.3 Score-based methods. ............................................................................ 29
2.5 Sentiment Classification Input Domain ........................................................ 30
2.5.1 Reviews. ................................................................................................. 30
2.5.2 Web discourse. ....................................................................................... 30
2.5.3 News articles. ......................................................................................... 31
2.5.4 Social media websites. ........................................................................... 31
2.6 Arabic Language Sentiment Classification ................................................... 31
2.7 Challenges and Issues.................................................................................... 33
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2.7.1 Difficulties inherent to sentiment analysis. ............................................ 34
2.7.2 Difficulties related to the Arabic language. ........................................... 35
2.7.3 Difficulties pertaining to social media text. ........................................... 36
2.8 Discussion and Direction .............................................................................. 37
3. System Description ............................................................................................... 39
3.1 ASSA Model Generation .............................................................................. 39
3.1.1 Data acquisition phase. .......................................................................... 40
3.1.2 Tweet-filtering phase. ............................................................................ 40
3.1.3 Annotation phase. .................................................................................. 40
3.1.4 Data preprocessing phase. ...................................................................... 40
3.1.5 Feature identification phase. .................................................................. 40
3.1.6 Classification phase. .............................................................................. 41
3.2 ASSA System Deployment ........................................................................... 41
3.2.1 Data acquisition phase. .......................................................................... 41
3.2.2 Data preprocessing phase. ...................................................................... 42
3.2.3 Feature identification phase. .................................................................. 42
3.2.4 Classification phase. .............................................................................. 42
4 Data Acquisition and Annotation Phases ............................................................. 43
4.1 Data Acquisition Phase ................................................................................. 43
4.1.1 Twitter fetcher module. .......................................................................... 43
4.2 Tweet-Filtering Phase ................................................................................... 45
4.3 Annotation Phase........................................................................................... 48
5 Data Preprocessing Phase ..................................................................................... 51
5.1 Data Cleaning ................................................................................................ 51
5.1.1 Tag adder module. ................................................................................. 52
5.1.2 Normalization module. .......................................................................... 52
5.1.3 Spam detection module. ......................................................................... 55
5.2 Natural Language Processing Tasks ............................................................. 59
5.2.1 Stemming. .............................................................................................. 59
5.2.2 Part-of-speech tagging. .......................................................................... 61
6 Feature Identification ............................................................................................ 63
6.1 Features extraction ........................................................................................ 63
6.1.1 Syntactic features. .................................................................................. 63
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Segmentation. ....................................................................................... 63
Punctuation. .......................................................................................... 64
6.1.2 Semantic features. .................................................................................. 64
6.1.3 Stylistic features. .................................................................................... 65
Word frequency. ..................................................................................... 65
Other stylistic attributes ......................................................................... 67
6.2 Non-lexical Features Extraction Experiments............................................... 67
6.2.1 First experiment. .................................................................................... 68
6.2.2 Second experiment. ................................................................................ 68
6.2.3 Third experiment. ................................................................................... 70
6.2.4 Fourth experiment. ................................................................................. 70
6.2.5 Fifth experiment. .................................................................................... 72
6.2.6 Sixth experiment. ................................................................................... 74
6.3 Features selection .......................................................................................... 75
6.3.1 N-gram selection. ................................................................................... 76
6.3.2 Word frequency selection. ..................................................................... 76
6.3.3 Lexical and non-lexical features selection. ............................................ 76
6.3.4 Three-way classification features selection. .......................................... 78
6.3.5 Subjectivity classification features selection. ........................................ 79
6.3.6 Polarity classification features selection. ............................................... 80
7 Classification Phase .............................................................................................. 84
7.1 Three-way Classification Accuracy Using Blind Testing ............................. 84
7.2 Subjectivity Classification Accuracy Using Blind Testing ........................... 85
7.3 Polarity Classification Accuracy Using Blind Testing ................................. 86
8 System Deployment .............................................................................................. 88
8.1 Data Acquisition Phase ................................................................................. 88
8.2 Data Preprocessing Phase.............................................................................. 89
8.3 Feature Identification Phase .......................................................................... 89
8.4 Classification Phase....................................................................................... 89
9 Conclusion ............................................................................................................ 90
9.1 Achievements ................................................................................................ 90
9.2 Limitations .................................................................................................... 91
9.3 Recommendations and Future Work ............................................................. 92
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10 References ......................................................................................................... 94
Appendix .................................................................................................................... 101
Twitter API ............................................................................................................. 101
YouTube API ......................................................................................................... 102
Regular Expressions ............................................................................................... 103
Sample of the Arabic Polarity Lexicon .................................................................. 104
Sample of Named Entity Dictionary ...................................................................... 105
11 Vita .................................................................................................................. 106

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