The Corpus Based Approach to Sentiment Analysis in Modern Standard Arabic and Arabic Dialects: A Literature Review
Öz
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.
Anahtar Kelimeler
Kaynakça
- [1] 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).
- [2] Ravi K. and Ravi V., "A Survey on Opinion Mining and Sentiment Analysis: Tasks, Approaches and Applications", Knowledge-Based Systems, 89: 14-46, (2015)
- [3] Bhadane C., Dalal H., and Doshi H., "Sentiment Analysis: Measuring Opinions", Procedia Computer Science, 45: 808-814, (2015)
- [4] 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)
- [5] Internet: WEEDOO, Twitter Arab World – Statistics Feb 2017, 2017, Available: https://weedoo.tech/twitter-arab-world-statistics-feb-2017/, Accessed: 29 July 2017
- [6] Internet: WEEDOO, Facebook Arab World – Statistics Feb 2017, 2017, Available: https://weedoo.tech/facebook-arab-world-statistics-feb-2017/, Accessed: 29 July 2017
- [7] 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)
- [8] 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).
Ayrıntılar
Birincil Dil
İngilizce
Konular
Mühendislik
Bölüm
Derleme
Yayımlanma Tarihi
1 Haziran 2018
Gönderilme Tarihi
13 Ekim 2017
Kabul Tarihi
-
Yayımlandığı Sayı
Yıl 2018 Cilt: 21 Sayı: 2
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