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Turkish Sentiment Analysis System via Ensemble Learning

Yıl 2020, Ejosat Özel Sayı 2020 (HORA), 122 - 129, 15.08.2020
https://doi.org/10.31590/ejosat.779181

Öz

Nowadays, sentiment analysis (SA) also known as opinion mining (OM) is widely used and has an impressive effect in many fields, such as marketing, politics, and even, company’s products are now adjusted based on users’ opinions. In this paper, a new efficient sentiment analysis system that supports the Turkish language has been introduced. In addition, as Turkish is an agglutinative language, which requires special processing, an efficient preprocessing model was also implemented as a part of the developed system.
Several experiments using the challenging and benchmark “The Turkish movie reviews” dataset have been conducted, and it is obvious that the constructed approach can efficiently support the Turkish language and can achieve a quite good performance.

Kaynakça

  • Social Media Examiner, "2019 Social Media Marketing Industry Report ", 2020 [Online]. Available: https://www.socialmediaexaminer.com/social-media-marketing-industry-report-2019,[Accessed: 20.1.2020].
  • Hussein, D. M. E. D. M. (2018). A survey on sentiment analysis challenges. Journal of King Saud University-Engineering Sciences, 30(4), 330-338.
  • Kaur, H., & Mangat, V. (2017, February). A survey of sentiment analysis techniques. In 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) (pp. 921-925). IEEE.
  • Wang, H., & Zhai, C. (2017). Generative models for sentiment analysis and opinion mining. In A practical guide to sentiment analysis (pp. 107-134). Springer, Cham.
  • Liu, R., Shi, Y., Ji, C., & Jia, M. (2019). A Survey of Sentiment Analysis Based on Transfer Learning. IEEE Access, 7, 85401-85412.
  • Ghorbel, H., & Jacot, D. (2011). Sentiment analysis of French movie reviews. In Advances in Distributed Agent-Based Retrieval Tools (pp. 97-108). Springer, Berlin, Heidelberg.
  • Esuli, A., & Sebastiani, F. (2006, May). Sentiwordnet: A publicly available lexical resource for opinion mining. In LREC (Vol. 6, pp. 417-422).
  • Eroğul, U. (2009). Sentiment analysis in Turkish (Master's thesis). Middle East Technical University, Ankara.
  • Vural, A. G., Cambazoglu, B. B., Senkul, P., & Tokgoz, Z. O. (2013). A framework for sentiment analysis in Turkish: Application to polarity detection of movie reviews in Turkish. In Computer and Information Sciences III (pp. 437-445). Springer, London.
  • Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010). Sentiment strength detection in short informal text. Journal of the American society for information science and technology, 61(12), 2544-2558.
  • Tofighy, S., & Fakhrahmad, S. M. (2018). A proposed scheme for sentiment analysis. Kybernetes.
  • Dehkharghani, R., Yanikoglu, B., Saygin, Y., & Oflazer, K. (2017). Sentiment analysis in Turkish at different granularity levels. Natural Language Engineering, 23(4), 535-559.
  • Türkmenoglu, C., & Tantug, A. C. (2014, June). Sentiment analysis in Turkish media. In International Conference on Machine Learning (ICML).
  • Kaya, M., Fidan, G., & Toroslu, I. H. (2012, December). Sentiment analysis of turkish political news. In 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (Vol. 1, pp. 174-180). IEEE.
  • Kaya, M. (2013). Sentiment analysis of Turkish political columns with transfer learning. Diss, Middle East Technical University.
  • Demirtas, E., & Pechenizkiy, M. (2013, August). Cross-lingual polarity detection with machine translation. In Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining (pp. 1-8).
  • Eryiğit, G., & Adalı, E. (2004, February). An affix stripping morphological analyzer for Turkish. In Proceedings of the IASTED international conference artificial intelligence and applications (pp. 299-304).
  • Yıldırım, E., Çetin, F. S., Eryiğit, G., & Temel, T. (2015). The impact of NLP on Turkish sentiment analysis. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi, 7(1), 43-51.
  • Rong, X. (2014). word2vec parameter learning explained. arXiv preprint arXiv:1411.2738.
  • Wang, H. (2014). Introduction to Word2vec and its application to find predominant word senses. URL: http://compling. hss. ntu. edu. sg/courses/hg7017/pdf/word2vec% 20and% 20its% 20appli cation% 20to% 20wsd. pdf.
  • Pennington, J., Socher, R., & Manning, C. D. (2014, October). Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532-1543).
  • Joulin, A., Grave, E., Bojanowski, P., Douze, M., Jégou, H., & Mikolov, T. (2016). Fasttext. zip: Compressing text classification models. arXiv preprint arXiv:1612.03651.
  • Onan, A., Korukoğlu, S., & Bulut, H. (2016). Ensemble of keyword extraction methods and classifiers in text classification. Expert Systems with Applications, 57, 232-247.
  • Freeman, E. A., Moisen, G. G., Coulston, J. W., & Wilson, B. T. (2016). Random forests and stochastic gradient boosting for predicting tree canopy cover: comparing tuning processes and model performance. Canadian Journal of Forest Research, 46(3), 323-339.
  • Kuncheva, L. I., & Rodríguez, J. J. (2007, May). An experimental study on rotation forest ensembles. In International workshop on multiple classifier systems (pp. 459-468). Springer, Berlin, Heidelberg

Turkish Sentiment Analysis System via Ensemble Learning

Yıl 2020, Ejosat Özel Sayı 2020 (HORA), 122 - 129, 15.08.2020
https://doi.org/10.31590/ejosat.779181

Öz

Nowadays, sentiment analysis (SA) also known as opinion mining (OM) is widely used and has an impressive effect in many fields, such as marketing, politics, and even, company’s products are now adjusted based on users’ opinions. In this paper, a new efficient sentiment analysis system that supports the Turkish language has been introduced. In addition, as Turkish is an agglutinative language, which requires special processing, an efficient preprocessing model was also implemented as a part of the developed system.
Several experiments using the challenging and benchmark “The Turkish movie reviews” dataset have been conducted, and it is obvious that the constructed approach can efficiently support the Turkish language and can achieve a quite good performance.

Kaynakça

  • Social Media Examiner, "2019 Social Media Marketing Industry Report ", 2020 [Online]. Available: https://www.socialmediaexaminer.com/social-media-marketing-industry-report-2019,[Accessed: 20.1.2020].
  • Hussein, D. M. E. D. M. (2018). A survey on sentiment analysis challenges. Journal of King Saud University-Engineering Sciences, 30(4), 330-338.
  • Kaur, H., & Mangat, V. (2017, February). A survey of sentiment analysis techniques. In 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) (pp. 921-925). IEEE.
  • Wang, H., & Zhai, C. (2017). Generative models for sentiment analysis and opinion mining. In A practical guide to sentiment analysis (pp. 107-134). Springer, Cham.
  • Liu, R., Shi, Y., Ji, C., & Jia, M. (2019). A Survey of Sentiment Analysis Based on Transfer Learning. IEEE Access, 7, 85401-85412.
  • Ghorbel, H., & Jacot, D. (2011). Sentiment analysis of French movie reviews. In Advances in Distributed Agent-Based Retrieval Tools (pp. 97-108). Springer, Berlin, Heidelberg.
  • Esuli, A., & Sebastiani, F. (2006, May). Sentiwordnet: A publicly available lexical resource for opinion mining. In LREC (Vol. 6, pp. 417-422).
  • Eroğul, U. (2009). Sentiment analysis in Turkish (Master's thesis). Middle East Technical University, Ankara.
  • Vural, A. G., Cambazoglu, B. B., Senkul, P., & Tokgoz, Z. O. (2013). A framework for sentiment analysis in Turkish: Application to polarity detection of movie reviews in Turkish. In Computer and Information Sciences III (pp. 437-445). Springer, London.
  • Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010). Sentiment strength detection in short informal text. Journal of the American society for information science and technology, 61(12), 2544-2558.
  • Tofighy, S., & Fakhrahmad, S. M. (2018). A proposed scheme for sentiment analysis. Kybernetes.
  • Dehkharghani, R., Yanikoglu, B., Saygin, Y., & Oflazer, K. (2017). Sentiment analysis in Turkish at different granularity levels. Natural Language Engineering, 23(4), 535-559.
  • Türkmenoglu, C., & Tantug, A. C. (2014, June). Sentiment analysis in Turkish media. In International Conference on Machine Learning (ICML).
  • Kaya, M., Fidan, G., & Toroslu, I. H. (2012, December). Sentiment analysis of turkish political news. In 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (Vol. 1, pp. 174-180). IEEE.
  • Kaya, M. (2013). Sentiment analysis of Turkish political columns with transfer learning. Diss, Middle East Technical University.
  • Demirtas, E., & Pechenizkiy, M. (2013, August). Cross-lingual polarity detection with machine translation. In Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining (pp. 1-8).
  • Eryiğit, G., & Adalı, E. (2004, February). An affix stripping morphological analyzer for Turkish. In Proceedings of the IASTED international conference artificial intelligence and applications (pp. 299-304).
  • Yıldırım, E., Çetin, F. S., Eryiğit, G., & Temel, T. (2015). The impact of NLP on Turkish sentiment analysis. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi, 7(1), 43-51.
  • Rong, X. (2014). word2vec parameter learning explained. arXiv preprint arXiv:1411.2738.
  • Wang, H. (2014). Introduction to Word2vec and its application to find predominant word senses. URL: http://compling. hss. ntu. edu. sg/courses/hg7017/pdf/word2vec% 20and% 20its% 20appli cation% 20to% 20wsd. pdf.
  • Pennington, J., Socher, R., & Manning, C. D. (2014, October). Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532-1543).
  • Joulin, A., Grave, E., Bojanowski, P., Douze, M., Jégou, H., & Mikolov, T. (2016). Fasttext. zip: Compressing text classification models. arXiv preprint arXiv:1612.03651.
  • Onan, A., Korukoğlu, S., & Bulut, H. (2016). Ensemble of keyword extraction methods and classifiers in text classification. Expert Systems with Applications, 57, 232-247.
  • Freeman, E. A., Moisen, G. G., Coulston, J. W., & Wilson, B. T. (2016). Random forests and stochastic gradient boosting for predicting tree canopy cover: comparing tuning processes and model performance. Canadian Journal of Forest Research, 46(3), 323-339.
  • Kuncheva, L. I., & Rodríguez, J. J. (2007, May). An experimental study on rotation forest ensembles. In International workshop on multiple classifier systems (pp. 459-468). Springer, Berlin, Heidelberg
Toplam 25 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Saed Alqaraleh Bu kişi benim 0000-0002-7146-3905

Yayımlanma Tarihi 15 Ağustos 2020
Yayımlandığı Sayı Yıl 2020 Ejosat Özel Sayı 2020 (HORA)

Kaynak Göster

APA Alqaraleh, S. (2020). Turkish Sentiment Analysis System via Ensemble Learning. Avrupa Bilim Ve Teknoloji Dergisi122-129. https://doi.org/10.31590/ejosat.779181