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The Impact of NLP on Turkish Sentiment Analysis

Yıl 2014, Cilt: 7 Sayı: 1, 43 - 51, 02.11.2014

Öz

Sentiment analysis on English texts is a highly popular and well-studied topic. On the other hand, the research in this field for morphologically rich languages is still in its infancy. Turkish is an agglutinative language with a very rich morphological structure. For the first time in the literature, this paper investigates and reports the impact of the natural language preprocessing layers on the sentiment analysis of Turkish social media texts. The experiments show that the sentiment analysis performance may be improved by nearly 5 percentage points yielding a success ratio of 78.83% on the used data set.

Kaynakça

  • [1] Muhammad Abdul-Mageed, Mona Diab, and Sandra Kübler. 2014. Samar: Subjectivity and sentiment analysis for Arabic social media. Computer Speech & Language, 28(1):20–37.
  • [2] Alexandra Balahur, Rada Mihalcea, and Andrés Montoyo. 2014. Computational approaches to subjectivity and sentiment analysis: Present and envisaged methods and applications. Computer Speech & Language, 28(1):1–6.
  • [3] Zeynep Boynukalin. 2012. Emotion analysis of Turkish texts by using machine learning methods. Ms.
  • [4] Rebecca F Bruce and Janyce M Wiebe. 1999. Recognizing subjectivity: a case study in manual tagging. Natural Language Engineering, 5(2):187–205.
  • [5] Mahmut Çetin and M Fatih Amasyali. 2013. Active learning for Turkish sentiment analysis. In Innovations in Intelligent Systems and Applications (INISTA), 2013 IEEE International Symposium on, pages 1–4. IEEE.
  • [6] Mahmut Çetin and M Fatih Amasyali. 2013. Supervised and traditional term weighting methods for sentiment analysis. In Signal Processing and Communications Applications Conference (SIU), 2013 21st, pages 1–4. IEEE.
  • [7] Gülşen Eryiğit, Fatih Samet Çetin, Meltem Yanik, Tanel Temel, and Ilyas Çiçekli. 2013. Turksent: A sentiment annotation tool for social media. In Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse, pages 131– 134, Sofia, Bulgaria, August. Association for Computational Linguistics.
  • [8] Gülşen Eryiğit. 2014. ITU Turkish NLP web service. In Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics (EACL), Gothenburg, Sweden, April. Association for Computational Linguistics.
  • [9] Minqing Hu and Bing Liu. 2004. Mining and summarizing customer reviews. In Proceedings of tConference on Knowledge Discovery and Data Mining, KDD ’04, pages 168–177, New York, NY, USA. ACM.
  • [10] Hayeon Jang and Hyopil Shin. 2010. Language specific sentiment analysis in morphologically rich languages. In Proceedings of the 23rd International Conference on Computational Linguistics: Posters, COLING ’10, pages 498–506, Stroudsburg, PA, USA. Association for Computational Linguistics.
  • [11] Mesut Kaya, Guven Fidan, and I Hakkı Toroslu. 2013. Transfer learning using twitter data for improving sentiment classification of Turkish political news. In Information Sciences and Systems 2013, pages 139–148. Springer.
  • [12] Soo-Min Kim and Eduard Hovy. 2004. Determining the sentiment of opinions. In Proceedings of the 20th international conference on Computational Linguistics, page 1367. Association for Computational Linguistics.
  • [13] Bing Liu. 2012. Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1):1–167.
  • [14] Satoshi Morinaga, Kenji Yamanishi, Kenji Tateishi, and Toshikazu Fukushima. 2002. Mining product reputations on the web. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 341–349. ACM.
  • [15] Sadi Evren Seker and Khaled Al-naami. 2013. Sentimental analysis on Turkish blogs via ensemble classifier. In Proceedings Of The 2013 International Conference On Data Mining. DMIN.
  • [16] Dilara Torunoğlu and Gülşen Eryiğit. 2014. A cascaded approach for social media text normalization of Turkish. In 5th Workshop on Language Analysis for Social Media (LASM) at EACL, Gothenburg, Sweden, April. Association for Computational Linguistics.
  • [17] A Gural Vural, B Barla Cambazoglu, Pinar Senkul, and Z Ozge Tokgoz. 2013. A framework for sentiment analysis in Turkish: Application to polarity detection of movie reviews in Turkish. In Computer and Information Sciences III, pages 437–445. Springer.
  • [18] Janyce M Wiebe, Rebecca F Bruce, and Thomas P O’Hara. 1999. Development and use of a gold standard data set for subjectivity classifications. In Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics, pages 246–253. Association for Computational Linguistics.
  • [19] Janyce Wiebe. 2000. Learning subjective adjectives from corpora. In AAAI/IAAI, pages 735–740.
  • [20] Michael Wiegand, Alexandra Balahur, Benjamin Roth, Dietrich Klakow, and Andrés Montoyo. 2010. A survey on the role of negation in sentiment analysis. In Proceedings of the Workshop on Negation and Speculation in Natural Language Processing, NeSp-NLP’10, pages 60–68, Stroudsburg, PA, USA. Association for Computational Linguistics.
  • [21] Theresa Wilson, Janyce Wiebe, and Paul Hoffmann. 2005. Recognizing contextual polarity in phrase level sentiment analysis. In Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, HLT ’05, pages 347–354, Stroudsburg, PA, USA. Association for Computational Linguistics.
  • [22] Jeonghee Yi, Tetsuya Nasukawa, Razvan Bunescu, and Wayne Niblack. 2003. Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. In Data Mining, 2003. ICDM 2003. Third IEEE International Conference on, pages 427–434. IEEE.
  • [23] Hong Yu and Vasileios Hatzivassiloglou. 2003. Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences. In Proceedings of the 2003 conference on Empirical methods in natural language processing, pages 129–136. Association for Computational Linguistics. 13TÜRKİYE BİLİŞİM VAKFI BİLGİSAYAR he Tenth ACM SIGKDD International
Yıl 2014, Cilt: 7 Sayı: 1, 43 - 51, 02.11.2014

Öz

Kaynakça

  • [1] Muhammad Abdul-Mageed, Mona Diab, and Sandra Kübler. 2014. Samar: Subjectivity and sentiment analysis for Arabic social media. Computer Speech & Language, 28(1):20–37.
  • [2] Alexandra Balahur, Rada Mihalcea, and Andrés Montoyo. 2014. Computational approaches to subjectivity and sentiment analysis: Present and envisaged methods and applications. Computer Speech & Language, 28(1):1–6.
  • [3] Zeynep Boynukalin. 2012. Emotion analysis of Turkish texts by using machine learning methods. Ms.
  • [4] Rebecca F Bruce and Janyce M Wiebe. 1999. Recognizing subjectivity: a case study in manual tagging. Natural Language Engineering, 5(2):187–205.
  • [5] Mahmut Çetin and M Fatih Amasyali. 2013. Active learning for Turkish sentiment analysis. In Innovations in Intelligent Systems and Applications (INISTA), 2013 IEEE International Symposium on, pages 1–4. IEEE.
  • [6] Mahmut Çetin and M Fatih Amasyali. 2013. Supervised and traditional term weighting methods for sentiment analysis. In Signal Processing and Communications Applications Conference (SIU), 2013 21st, pages 1–4. IEEE.
  • [7] Gülşen Eryiğit, Fatih Samet Çetin, Meltem Yanik, Tanel Temel, and Ilyas Çiçekli. 2013. Turksent: A sentiment annotation tool for social media. In Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse, pages 131– 134, Sofia, Bulgaria, August. Association for Computational Linguistics.
  • [8] Gülşen Eryiğit. 2014. ITU Turkish NLP web service. In Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics (EACL), Gothenburg, Sweden, April. Association for Computational Linguistics.
  • [9] Minqing Hu and Bing Liu. 2004. Mining and summarizing customer reviews. In Proceedings of tConference on Knowledge Discovery and Data Mining, KDD ’04, pages 168–177, New York, NY, USA. ACM.
  • [10] Hayeon Jang and Hyopil Shin. 2010. Language specific sentiment analysis in morphologically rich languages. In Proceedings of the 23rd International Conference on Computational Linguistics: Posters, COLING ’10, pages 498–506, Stroudsburg, PA, USA. Association for Computational Linguistics.
  • [11] Mesut Kaya, Guven Fidan, and I Hakkı Toroslu. 2013. Transfer learning using twitter data for improving sentiment classification of Turkish political news. In Information Sciences and Systems 2013, pages 139–148. Springer.
  • [12] Soo-Min Kim and Eduard Hovy. 2004. Determining the sentiment of opinions. In Proceedings of the 20th international conference on Computational Linguistics, page 1367. Association for Computational Linguistics.
  • [13] Bing Liu. 2012. Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1):1–167.
  • [14] Satoshi Morinaga, Kenji Yamanishi, Kenji Tateishi, and Toshikazu Fukushima. 2002. Mining product reputations on the web. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 341–349. ACM.
  • [15] Sadi Evren Seker and Khaled Al-naami. 2013. Sentimental analysis on Turkish blogs via ensemble classifier. In Proceedings Of The 2013 International Conference On Data Mining. DMIN.
  • [16] Dilara Torunoğlu and Gülşen Eryiğit. 2014. A cascaded approach for social media text normalization of Turkish. In 5th Workshop on Language Analysis for Social Media (LASM) at EACL, Gothenburg, Sweden, April. Association for Computational Linguistics.
  • [17] A Gural Vural, B Barla Cambazoglu, Pinar Senkul, and Z Ozge Tokgoz. 2013. A framework for sentiment analysis in Turkish: Application to polarity detection of movie reviews in Turkish. In Computer and Information Sciences III, pages 437–445. Springer.
  • [18] Janyce M Wiebe, Rebecca F Bruce, and Thomas P O’Hara. 1999. Development and use of a gold standard data set for subjectivity classifications. In Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics, pages 246–253. Association for Computational Linguistics.
  • [19] Janyce Wiebe. 2000. Learning subjective adjectives from corpora. In AAAI/IAAI, pages 735–740.
  • [20] Michael Wiegand, Alexandra Balahur, Benjamin Roth, Dietrich Klakow, and Andrés Montoyo. 2010. A survey on the role of negation in sentiment analysis. In Proceedings of the Workshop on Negation and Speculation in Natural Language Processing, NeSp-NLP’10, pages 60–68, Stroudsburg, PA, USA. Association for Computational Linguistics.
  • [21] Theresa Wilson, Janyce Wiebe, and Paul Hoffmann. 2005. Recognizing contextual polarity in phrase level sentiment analysis. In Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, HLT ’05, pages 347–354, Stroudsburg, PA, USA. Association for Computational Linguistics.
  • [22] Jeonghee Yi, Tetsuya Nasukawa, Razvan Bunescu, and Wayne Niblack. 2003. Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. In Data Mining, 2003. ICDM 2003. Third IEEE International Conference on, pages 427–434. IEEE.
  • [23] Hong Yu and Vasileios Hatzivassiloglou. 2003. Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences. In Proceedings of the 2003 conference on Empirical methods in natural language processing, pages 129–136. Association for Computational Linguistics. 13TÜRKİYE BİLİŞİM VAKFI BİLGİSAYAR he Tenth ACM SIGKDD International
Toplam 23 adet kaynakça vardır.

Ayrıntılar

Diğer ID JA37MA33FU
Bölüm Makaleler(Araştırma)
Yazarlar

Ezgi Yıldırım Bu kişi benim

Fatih Samet Çetin Bu kişi benim

Gülşen Eryiğit Bu kişi benim

Tanel Temel Bu kişi benim

Yayımlanma Tarihi 2 Kasım 2014
Yayımlandığı Sayı Yıl 2014 Cilt: 7 Sayı: 1

Kaynak Göster

APA Yıldırım, E., Çetin, F. S., Eryiğit, G., Temel, T. (2014). The Impact of NLP on Turkish Sentiment Analysis. Türkiye Bilişim Vakfı Bilgisayar Bilimleri Ve Mühendisliği Dergisi, 7(1), 43-51.
AMA Yıldırım E, Çetin FS, Eryiğit G, Temel T. The Impact of NLP on Turkish Sentiment Analysis. TBV-BBMD. Kasım 2014;7(1):43-51.
Chicago Yıldırım, Ezgi, Fatih Samet Çetin, Gülşen Eryiğit, ve Tanel Temel. “The Impact of NLP on Turkish Sentiment Analysis”. Türkiye Bilişim Vakfı Bilgisayar Bilimleri Ve Mühendisliği Dergisi 7, sy. 1 (Kasım 2014): 43-51.
EndNote Yıldırım E, Çetin FS, Eryiğit G, Temel T (01 Kasım 2014) The Impact of NLP on Turkish Sentiment Analysis. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi 7 1 43–51.
IEEE E. Yıldırım, F. S. Çetin, G. Eryiğit, ve T. Temel, “The Impact of NLP on Turkish Sentiment Analysis”, TBV-BBMD, c. 7, sy. 1, ss. 43–51, 2014.
ISNAD Yıldırım, Ezgi vd. “The Impact of NLP on Turkish Sentiment Analysis”. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi 7/1 (Kasım 2014), 43-51.
JAMA Yıldırım E, Çetin FS, Eryiğit G, Temel T. The Impact of NLP on Turkish Sentiment Analysis. TBV-BBMD. 2014;7:43–51.
MLA Yıldırım, Ezgi vd. “The Impact of NLP on Turkish Sentiment Analysis”. Türkiye Bilişim Vakfı Bilgisayar Bilimleri Ve Mühendisliği Dergisi, c. 7, sy. 1, 2014, ss. 43-51.
Vancouver Yıldırım E, Çetin FS, Eryiğit G, Temel T. The Impact of NLP on Turkish Sentiment Analysis. TBV-BBMD. 2014;7(1):43-51.

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