Modern Standart Arapça ve Arap Lehçelerinde Duygu Analizine Külliyat (Korpus) Temelli Yaklaşım: Bir Literatür İncelemesi
Duygu analizi; kişilerin ürünler, servisler, firmalar, bireyler, görevler, olaylar, başlıklar ve bunların özellikleri üzerine fikirleri, duyguları, değerlendirmeleri, değer biçmeleri, tutumları ve hislerini analiz edilmesidir. Internet ve sosyal ağlardaki uygulamaların artmasıyla birlikte, Duygu Analizi (DA), metin madenciliği araştırma alanında dikkate değer bir konuma gelmiş ve o zamandan beri, kullanıcıların Internet üzerinden tartışılan çeşitli ürünler veya konular hakkındaki görüşlerini keşfetmek için kullanılmaktadır. Duygu Analizi üzerine yapılan çalışmalar incelendiğinde analize temel oluşturan Internet bilgi kaynakları doğal dilinin çoğunlukla İngilizce olduğu görülmektedir. Doğal Dil İşleme ve Hesaplamalı Dil Bilim alanlarındaki gelişmeler İngilizce dışındaki doğal dillerden yapılan Duygu Analizi çalışmalarına olumlu katkıları olmuştur. Bu çalışmanın amacı, Arapça içerikli Internet bilgi kaynaklarından gerçekleştirilen Duygu Analizi literaturü incelemektir. Literatür incelemesi, Arapça Internet bilgi kaynaklarından oluşturulan külliyat (corpus) yaklaşımına dayanan çalışmaları kapsamaktadır. Hem Modern Standart Arapça, hem de Arap lehçeleri için kendi külliyatlarını(corpora) oluşturan ve bu metinler üzerinden duygu analizi yapılan çalışmalar incelenmektedir.
The Corpus Based Approach to Sentiment Analysis in Modern Standard Arabic and Arabic Dialects: A Literature Review
Sentiment Analysis, is the analysis of ideas, emotions, evaluations, values, attitudes and feelings about products, services, companies, individuals, tasks, events, titles and their characteristics. With the increase in applications on the Internet and social networks, Sentiment Analysis has taken a considerable place in the field of text mining research and has since been used to explore the opinions of users about various products or topics discussed over the Internet. When the literature on Sentiment Analysis is examined, it is seen that the natural language of the Internet information sources that form the basis of the analysis is mostly English. Developments in the fields of Natural Language Processing and Computational Linguistics have contributed positively to Sentiment Analysis studies made from natural languages other than English. The purpose of this study is to examine the literature of Sentiment Analysis conducted in Arabic internet information sources. The literature review includes studies based on the corpus approach, which is made up of Arabic Internet information sources. Studies are being carried out on the works which constitute their own corpora for both Modern Standard Arabic and Arabic dialects and on which sentiment analysis is performed.
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- Aliane A., Aliane H., Ziane M., and Bensaou N., "A
Genetic Algorithm Feature Selection Based Approach for
Arabic Sentiment Classification", 2016 IEEE/ACS 13th
International Conference of Computer Systems and
Applications (AICCSA), Agadir, Morocco, 1-6, (2016).
- Ravi K. and Ravi V., "A Survey on Opinion Mining and
Sentiment
Analysis:
Tasks,
Approaches
and
Applications", Knowledge-Based Systems, 89: 14-46,
(2015)
- Bhadane C., Dalal H., and Doshi H., "Sentiment
Analysis: Measuring Opinions", Procedia Computer
Science, 45: 808-814, (2015)
- Alhumoud S. O., Altuwaijri M. I., Albuhairi T. M., and
Alohaideb W. M., "Survey on Arabic Sentiment Analysis
in Twitter", International Journal of Social, Behavioral,
Educational, Economic, Business and Industrial
Engineering 9: 364-368, (2015)
- Internet: WEEDOO, Twitter Arab World – Statistics Feb
2017, 2017, Available: https://weedoo.tech/twitter-arab-
world-statistics-feb-2017/, Accessed: 29 July 2017
- Internet: WEEDOO, Facebook Arab World – Statistics
Feb
2017,
2017,
Available:
https://weedoo.tech/facebook-arab-world-statistics-feb-
2017/, Accessed: 29 July 2017
- Al-Kabi M. N., Gigieh A. H., Alsmadi I. M., Wahsheh H.
A., and Haidar M. M., "Opinion Mining and Analysis for
Arabic Language", International Journal of Advanced
Computer Science and Applications (IJACSA), 5: 181-
195, (2014)
- Hamed O. and Zesch T. "The Role of Diacritics in
Designing Lexical Recognition Tests for Arabic", In:
Proceedings of the 3rd International Conference on
Arabic Computational Linguistics, ACLing 2017,
Dubai, United Arab Emirates, 119-128, (2017).
- Biskri I., Berrakem F.-Z., and Jebali A. "The Applicative
Combinatory Categorial Analysis of Arabic", In:
Proceedings of the 3rd International Conference on
Arabic Computational Linguistics, ACLing 2017,
Dubai, United Arab Emirates, 199-207, (2017).
- Abuata B. and Al-Omari A., "A Rule-Based Stemmer for
Arabic Gulf Dialect", Journal of King Saud University-
Computer and Information Sciences, 27: 104-112,
(2015)
- Alshutayri A. and Atwell E., "Exploring Twitter as a
Source of an Arabic Dialect Corpus", International
Journal of Computational Linguistics (IJCL), 8: 37-44,
(2017)
- Medhat W., Hassan A., and Korashy H., "Sentiment
Analysis Algorithms and Applications: A Survey", Ain
Shams Engineering Journal, 5: 1093-1113, (2014)
- Boudad N., Faizi R., Thami R. O. H., and Chiheb R.,
"Sentiment Analysis in Arabic: A Review of the
Literature", Ain Shams Engineering Journal, (2017)
- Internet: UNESCO, Unesco World Arabic Language
Day,
2012,
Available:
http://www.unesco.org/new/en/unesco/events/prizes-
and-celebrations/celebrations/international-days/world-
arabic-language-day, Accessed: 23 March 2017
- Al-Kabi M. N., Abdulla N. A., and Al-Ayyoub M. "An
Analytical Study of Arabic Sentiments: Maktoob Case
Study", In: Proceedings of the 2013 8th International Conference for Internet Technology and Secured
Transactions (ICITST), London, UK, 89-94, (2013).
- Sharma A. and Dey S. "A Comparative Study of Feature
Selection and Machine Learning Techniques for
Sentiment Analysis",
In: Proceedings of the
Proceedings of the 2012 ACM research in applied
computation symposium, San Antonio, Texas,USA, 1-7,
(2012).
- Awwad H. and Alpkocak A. "Performance Comparison
of Different Lexicons for Sentiment Analysis in Arabic",
In: Proceedings of the 2016 Third European Network
Intelligence Conference (ENIC), Wrocław, Poland, 127-
133, (2016).
- Ibrahim M. A. and Salim N., "Opinion Analysis for
Twitter and Arabic Tweets: A Systematic Literature
Review", Journal of Theoretical & Applied Information
Technology, 56: (2013)
- Cherif W., Madani A., and Kissi M. "A New Modeling
Approach for Arabic Opinion Mining Recognition", In:
Proceedings of the 2015 Intelligent Systems and
Computer Vision (ISCV), Fez, Morocco, 1-6, (2015).
- Al-Smadi M., Qawasmeh O., Talafha B., and Quwaider
M. "Human Annotated Arabic Dataset of Book Reviews
for Aspect Based Sentiment Analysis", In: Proceedings
of the 2015 3rd International Conference on Future
Internet of Things and Cloud (FiCloud), Rome, Italy,
726-730, (2015).
- Aly M. A. and Atiya A. F. "Labr: A Large Scale Arabic
Book Reviews Dataset", In: Proceedings of the 51st
Annual Meeting of the Association for Computational
Linguistics, Sofia, Bulgaria, 494-498, (2013).
- Cherif W., Madani A., and Kissi M., "Towards an
Efficient Opinion Measurement in Arabic Comments",
Procedia Computer Science, 73: 122-129, (2015)
- AL-Smadi M., Al-Ayyoub M., Al-Sarhan H., and
Jararweh Y. "Using Aspect-Based Sentiment Analysis to
Evaluate Arabic News Affect on Readers",
In:
Proceedings of the 2015 IEEE/ACM 8th International
Conference on Utility and Cloud Computing (UCC),
Limassol, Cyprus, 436-441, (2015).
- Stenetorp P., Pyysalo S., Topić G., Ohta T., Ananiadou
S., and Tsujii J. i. "Brat: A Web-Based Tool for Nlp-
Assisted Text Annotation", In: Proceedings of the 13th
Conference of the European Chapter of the Association
for Computational Linguistics, Avignon, France, 102-
107, (2012).
- Althobaiti M., Kruschwitz U., and Poesio M. "Aranlp: A
Java-Based Library for the Processing of Arabic Text",
In: Proceedings of the Ninth International Conference
on Language Resources and Evaluation, Reykjavik,
Iceland, (2014).
- Al-Rfou R., Kulkarni V., Perozzi B., and Skiena S.
"Polyglot-Ner: Massive Multilingual Named Entity
Recognition", In: Proceedings of the Proceedings of the
2015 SIAM International Conference on Data Mining,
British Columbia, Canada, 586-594, (2015).
- Duwairi R. and Qarqaz I. "Arabic Sentiment Analysis
Using Supervised Classification", In: Proceedings of the
2014 International Conference on Future Internet of
Things and Cloud (FiCloud), Barcelona, Spain 579-583,
(2014).
- Duwairi R., Marji R., Shaban N., and Ershaidat S.,
"Sentiment Analysis", B.S. thesis, Jordan University of
Science and Technology, (2012).
- Duwairi R., Marji R., Sha'ban N., and Rushaidat S.
"Sentiment Analysis in Arabic Tweets", In: Proceedings
of the 2014 5th international conference on Information
and communication systems (ICICS), Irbid, Jordan, 1-6,
(2014).
-
Pedregosa F., Varoquaux G., Gramfort A., Michel V.,
Thirion B., Grisel O., et al., "Scikit-Learn: Machine
Learning in Python", Journal of Machine Learning
Research, 12: 2825-2830, (2011)
- Abdul-Mageed M. and Diab M. T. "Awatif: A Multi-
Genre Corpus for Modern Standard Arabic Subjectivity
and Sentiment Analysis", In: Proceedings of the eighth
international conference on Language Resources and
Evaluation (LREC), Istanbul, Turkey, 3907-3914,
(2012).
- Maamouri M., Bies A., Buckwalter T., and Mekki W.
"The Penn Arabic Treebank: Building a Large-Scale
Annotated Arabic Corpus", In: Proceedings of the
NEMLAR conference on Arabic language resources
and tools, Cairo, Egypt, 466-467, (2004).
- Rushdi‐Saleh M., Martín‐Valdivia M. T., Ureña‐López L.
A., and Perea‐Ortega J. M., "Oca: Opinion Corpus for
Arabic", Journal of the Association for Information
Science and Technology, 62: 2045-2054, (2011)
- Biadsy F., Hirschberg J., and Habash N. "Spoken Arabic
Dialect Identification Using Phonotactic Modeling", In:
Proceedings of the Proceedings of the eacl 2009
workshop on computational approaches to semitic
languages, Athens, Greece, 53-61, (2009).
- Itani M., Roast C., and Al-Khayatt S. "Corpora for
Sentiment Analysis of Arabic Text in Social Media", In:
Proceedings of the 2017 8th International Conference
on Information and Communication Systems (ICICS),
Irbid, Jordan, 64-69, (2017).
- Al-Rubaiee H., Qiu R., and Li D. "Identifying Mubasher
Software Products through Sentiment Analysis of Arabic
Tweets", In: Proceedings of the 2016 International
Conference on Industrial Informatics and Computer
Systems (CIICS), Sharjah, United Arab Emirates, 1-6,
(2016).
- Hathlian N. F. B. and Hafezs A. M. "Sentiment-
Subjective Analysis Framework for Arabic Social Media
Posts", In: Proceedings of the Saudi International
Conference on Information Technology (Big Data
Analysis) (KACSTIT), Riyadh, Saudi Arabia, 1-6,
(2016).
- Sghaier M. A. and Zrigui M. "Sentiment Analysis for
Arabic E-Commerce Websites", In: Proceedings of the
International Conference on Engineering & MIS
(ICEMIS), Agadir, Morocco, 1-7, (2016).
- Shoukry A. and Rafea A. "A Hybrid Approach for
Sentiment Classification of Egyptian Dialect Tweets",
In: Proceedings of the 2015 First International
Conference on Arabic Computational Linguistics
(ACLing), Cairo, Egypt, 78-85, (2015).
- Ibrahim H. S., Abdou S. M., and Gheith M. "Mika: A
Tagged Corpus for Modern Standard Arabic and
Colloquial Sentiment Analysis", In: Proceedings of the
2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS), , Kolkata,
India, 353-358, (2015).
- Abdulla N. A., Ahmed N. A., Shehab M. A., and Al-
Ayyoub M. "Arabic Sentiment Analysis: Lexicon-Based
and Corpus-Based", In: Proceedings of the 2013 IEEE
Jordan Conference on Applied Electrical Engineering
and Computing Technologies (AEECT), Amman,
Jordan, 1-6, (2013).
- Shoukry A. and Rafea A. "Sentence-Level Arabic
Sentiment Analysis", In: Proceedings of the 2012
International
Conference
on
Collaboration
Technologies and Systems (CTS), Denver, CO, USA,
546-550, (2012).