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Türkçe Metinlerde Duygu Analizi

Yıl 2021, Cilt: 16 Sayı: 63, 1514 - 1534, 31.07.2021
https://doi.org/10.19168/jyasar.928843

Öz

Günümüzde yaygınlaşan internet ve sosyal medya kullanımının artmasıyla ortaya çıkan büyük verinin, analiz edilerek anlamlı bilgiye dönüştürülmesi oldukça büyük bir öneme sahiptir. Duygu analizinde ise, bir metin içerisinde fikir içeren verinin sistematik olarak incelenmesi ve metne dair duygu kategorisinin ve duygu polaritesinin belirlenmesi sürecidir. Sadece dil biliminde değil, finansal piyasalar, pazarlama ve sosyal medya analizi gibi birçok farklı alanda sıklıkla duygu analizi yaklaşımları kullanılmaktadır. İngilizcenin dünyada konuşulan ortak dil olması sebebiyle, literatürde İngilizce metinler üzerine yapılmış birçok duygu analizi çalışması bulunmaktadır. Ancak, Türkçe metinlerde duygu analizi hâlen geliştirilmeye açık bir araştırma alanıdır. Bu çalışmada, Türkçe metinlerde duygu analizi literatürü incelenerek, literatürde sıklıkla kullanılan yöntemler, açık kaynaklı kütüphaneler ve veri tabanları ortaya konulmuş ve araştırmaya açık konular irdelenmiştir.

Kaynakça

  • Açıkalın, Utku Umur, Benan Bardak, Mucahid Kutlu. 2020. Turkish Sentiment Analysis Using BERT. 28th Signal Processing and Communications Applications Conference (SIU). IEEE: 1-4.
  • Aguwa, C., M.H. Olya ve L. Monplaisir. 2017. Modeling of fuzzy-based voice of customer for business decision analytics. Knowledge-Based Systems: 125, 136-145.
  • Ahmetoğlu, Hüseyin, and Resul Daş. 2020. “Türkçe Otel Yorumlarıyla Eğitilen Kelime Vektörü Modellerinin Duygu Analizi Ile İncelenmesi.” Süleyman Demirel üniversitesi Fen Bilimleri Enstitüsü Dergisi, August, 455–63.
  • Akba, Firat, Alaettin Uçan, Ebru Akcapinar Sezer, and Hayri Sever. 2014. "Assessment of feature selection metrics for sentiment analyses: Turkish movie reviews". In 8th European Conference on Data Mining. 191(2002): 180-184.
  • Akba, F. Assesment of Feature Selection Metrics for Sentiment Analysis: Turkish Movie Reviews, Published Master's Thesis, Ankara, (2014).
  • Akgül, Eyüp Sercan, Caner Ertano, and Banu Diri. 2016. “Sentiment analysis with Twitter.” Pamukkale University Journal of Engineering Sciences 22 (2): 106–10.
  • Akın, Ahmet Afsin, ve Mehmet Dündar Akın. 2007. "Zemberek, an open source nlp framework for turkic languages." Structure 10: 1-5.
  • Alpkoçak, Adil, Mansur Alp Tocoglu, Azer Çelikten, and İrfan Aygün. 2019. “Türkçe Metinlerde Duygu Analizi Için Farklı Makine Öğrenmesi Yöntemlerinin Karşılaştırılması.” Deu Muhendislik Fakultesi Fen ve Muhendislik 21 (63): 719–25.
  • Arazy, O. ve Woo, C. 2007. Enhancing information retrieval through statistical natural language processing: a study of collocation indexing, MIS Quarterly, 31(3): 525-546.
  • Atan, Suat, ve Yetkin Çınar. 2019. Borsa İstanbul’da finansal haberler ile piyasa değeri ilişkisinin metin madenciliği ve duygu (sentiment) analizi ile incelenmesi. Ankara Üniversitesi SBF Dergisi, 74(1): 1-34.
  • Baker, M., ve J. Wurgler. 2007. Investor sentiment in the stock market. Journal of economic perspectives, 21(2), 129-152.
  • Bayraktar, Kivanc, Uraz Yavanoglu, and Alper Ozbilen. 2019. “A Rule-Based Holistic Approach for Turkish Aspect-Based Sentiment Analysis.” 2019 IEEE International Conference on Big Data (Big Data). https://doi.org/10.1109/bigdata47090.2019.9005473.
  • Bermingham, A., ve Alan F. S. 2010. Classifying sentiment in microblogs: is brevity an advantage?. In Proceedings of the 19th ACM international conference on Information and knowledge management, 1833-1836.
  • Bilgin, Metin ve İzzet Fatih Şentürk. 2017. Sentiment analysis on Twitter data with semi-supervised Doc2Vec. In 2017 international conference on computer science and engineering (UBMK).IEEE: 661-666.
  • Burcu, Akın, ve Şimşek, Umma, Tuğba, Gürsoy. 2018. Sosyal Medya Analitiği İle Değer Yaratma: Duygu Analizi İle Geleceğe Yönelim. Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 5(3): 797-811.
  • Catal, Cagatay, and Mehmet Nangir. 2017. “A Sentiment Classification Model Based on Multiple Classifiers.” Applied Soft Computing 50 (January): 135–41.
  • Ceron, A., L. Curini, S. Iacus ve G. Porro. 2014. Every tweet counts? How sentiment analysis of social media can improve our knowledge of citizens’ political preferences with an application to Italy and France. New media & society, 16(2): 340-358.
  • Ciftci, Basri, and Mehmet Serkan Apaydin. 2018. “A Deep Learning Approach to Sentiment Analysis in Turkish.” 2018 International Conference on Artificial Intelligence and Data Processing (IDAP). https://doi.org/10.1109/idap.2018.8620751.
  • Çalı, Sedef, and Şebnem Yılmaz Balaman. 2019. "Improved decisions for marketing, supply and purchasing: Mining big data through an integration of sentiment analysis and intuitionistic fuzzy multi criteria assessment." Computers & Industrial Engineering 129: 315-332.
  • Catal, Cagatay, and Mehmet Nangir. 2017. "A sentiment classification model based on multiple classifiers." Applied Soft Computing 50: 135-141.
  • Çeti̇n, Fatih Samet, and Gülşen Eryiğit. 2018. “Türkçe Hedef Tabanlı Duygu Analizi İçin Alt Görevlerin İncelenmesi – Hedef Terim, Hedef Kategori Ve Duygu Sınıfı Belirleme.” Bilişim Teknolojileri Dergisi, January. https://doi.org/10.17671/gazibtd.325865.
  • Çoban, Önder, Baris Özyer, and Gülsah Tümüklü Özyer. 2015. “Türkçe Twitter Mesajlarının Duygu Analizi Sentiment Analysis for Turkish Twitter Feeds.” In 2015 23nd Signal Processing and Communications Applications Conference (SIU). IEEE. 2388-2391.
  • Dave, K., Lawrence, S. ve Pennock, D.M. 2003, Mining the peanut gallery: opinion extraction and semantic classification of product reviews, Proceedings of the 12th International World Wide Web, ACM, Budapeşte.
  • Dehkharghani, Rahim, Berrin Yanikoglu, Yucel Saygin, and Kemal Oflazer. 2017. “Sentiment Analysis in Turkish at Different Granularity Levels.” Natural Language Engineering 23 (4): 535–59.
  • Dehkharghani, Rahim, Yucel Saygin, Berrin Yanikoglu, and Kemal Oflazer. 2016. "SentiTurkNet: a Turkish polarity lexicon for sentiment analysis." Language Resources and Evaluation 50, no. 3: 667-685.
  • Dehkharghani, Rahim. 2018. “A Hybrid Approach to Generating Adjective Polarity Lexicon and Its Application to Turkish Sentiment Analysis.” International Journal of Modern Education & Computer Science, 10(11). https://doi.org/10.5815/ijmecs.201.11.0.
  • Dehkharghani, Rahim, Sentiment Analysis in Turkish: Resources and Techniques, Doktora Tezi, Sabancı University, İstanbul (2015).
  • Demirci, Gozde Merve, Seref Recep Keskin, and Gulustan Dogan. 2019. “Sentiment Analysis in Turkish with Deep Learning.” 2019 IEEE International Conference on Big Data (Big Data). https://doi.org/10.1109/bigdata47090.2019.9006066.
  • Dülger, Oğuzhan. 2018. “Türkçe Metinlerde İroni Tespiti.” Proceedings of the 12th Turkish National Software Engineering Symposium (UYMS).
  • Erşahi̇n, Buket, Özlem Aktaş, Deniz Kilinç, and Mustafa Erşahi̇n. 2019. “A Hybrid Sentiment Analysis Method for Turkish.” TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 27 (3): 1780–93.
  • Fan, Z. P., Che, Y. J., ve Chen, Z. Y. 2017. Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis. Journal of Business Research 74: 90-100.
  • Farías, Delia Irazú Hernańdez, Viviana Patti, and Paolo Rosso. "Irony detection in twitter: The role of affective content." ACM Transactions on Internet Technology (TOIT) 16, no. 3 (2016): 1-24.
  • Farías, Irazú Hernández, José-Miguel Benedí, and Paolo Rosso. "Applying basic features from sentiment analysis for automatic irony detection." In Iberian Conference on Pattern Recognition and Image Analysis, pp. 337-344. Springer, Cham, 2015.
  • Gözükara, Furkan, and Selma Ayşe Özel. 2016. “An Experimental Investigation of Document Vector Computation Methods for Sentiment Analysis of Turkish and English Reviews.”
  • Hine, C. 2005, Virtual Methods: Issues in Social Research on the Internet, Berg, Oxford.
  • İskender, Elyase, and Gülgönül Bozoğlu Batı. 2015. “Comparing Turkish Universities Entrepreneurship and Innovativeness Index’s Rankings with Sentiment Analysis Results on Social Media.” Procedia - Social and Behavioral Sciences 195 (July): 1543–52.
  • Kama, Batuhan, Murat Ozturk, Pinar Karagoz, I. Hakki Toroslu, and Murat Kalender. 2017. “Analyzing Implicit Aspects and Aspect Dependent Sentiment Polarity for Aspect-Based Sentiment Analysis on Informal Turkish Texts.” In Proceedings of the 9th International Conference on Management of Digital EcoSystems. New York, NY, USA: ACM. https://doi.org/10.1145/3167020.3167041.
  • Kamisli Ozturk, Z., Z. İ. Erzurum Cicek, and Z. Ergul. 2017. “Sentiment Analysis: An Application to Anadolu University.” Acta Physica Polonica Series a 132 (3): 753–55. Karagoz, Pinar, Batuhan Kama, Murat Ozturk, I. Hakki Toroslu, and Deniz Canturk. 2019. “A Framework for Aspect Based Sentiment Analysis on Turkish Informal Texts.” Journal of Intelligent Information Systems 53 (3): 431–51.
  • Karamollaoğlu, Hamdullah, İbrahim Alper Doğru, Murat Dörterler, Anıl Utku, Oktay Yıldız. 2018. Sentiment analysis on Turkish social media shares through lexicon based approach. In 2018 3rd International Conference on Computer Science and Engineering. IEEE. 45-49.
  • Karaöz, Burcu, and U. Tuğba Gürsoy. 2018. “Adaptif Öğrenme Sözlüğü Temelli Duygu Analiz Algoritması Önerisi.” Bilişim Teknolojileri Dergisi, August. https://doi.org/10.17671/gazibtd.342419.
  • Karcioğlu, Abdullah Ammar ve Tolga Aydin. 2019. Sentiment analysis of Turkish and english twitter feeds using Word2Vec model. In 2019 27th Signal Processing and Communications Applications Conference (SIU). IEEE: 1-4.
  • Kaya, M., G. Fidan, and I. H. Toroslu. 2012. “Sentiment Analysis of Turkish Political News.” In 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, 1:174–80.
  • Kaynar, Oguz, Yasin Görmez, Mustafa Yildiz, and Ayşegül Albayrak. 2016. “Makine Öğrenmesi Yöntemleri Ile Duygu Analizi.” In International Artificial Intelligence and Data Processing Symposium (IDAP'16): 17-18.
  • Keleş, Pervan Nergis; 2017. “Sentiment Analysis Using a Random Forest Classifier on Turkish Web Comments.” Communications Faculty Of Science University of Ankara. https://doi.org/10.1501/commua1-2_0000000105.
  • Kozinets, R. 2009, Netnography: Doing Ethnographic Research Online, Sage Publications, Londra.
  • Ku, L.W., Ho, H.W. and Chen, H.H. 2009. Opinion mining and relationship discovery using CopeOpi opinion analysis system, Journal of the American Society for Information Science and Technology, 60 (7): 1486-1503.
  • Kumar, A., ve A. Jaiswal. 2020. Systematic literature review of sentiment analysis on Twitter using soft computing techniques. Concurrency and Computation: Practice and Experience, 32(1): e5107.
  • Liu, Bing. 2012. "Sentiment analysis and opinion mining." Synthesis lectures on human language technologies 5, no. 1: 1-167.
  • Medhat, Walaa, Ahmed Hassan, and Hoda Korashy. "Sentiment analysis algorithms and applications: A survey." Ain Shams engineering journal 5, no. 4 (2014): 1093-1113.
  • Metin, Senem Kumova, and Bahar Karaoğlan. "Türkiye Türkçesinde Eşdizimlerin İstatistiksel Yöntemlerle Belirlenmesi." Bilig 78 (2016): 253-286.
  • Misopoulos, F., Mitic, M., Kapoulas, A. and Karapiperis, C. 2014. "Uncovering customer service experiences with Twitter: the case of airline industry", Management Decision, 52 (4): 705-723.
  • Nalçakan, Yağiz, Şan Sitki Bayramoğlu, and Samed Tuna. 2015. “Sosyal Medya Verileri Üzerinde Yapay Öğrenme Ile Duygu Analizi Çalışması.” Technical Report. Trakya Üniversitesi.
  • Nielsen, Finn Årup. "A new ANEW: Evaluation of a word list for sentiment analysis in microblogs." arXiv preprint arXiv:1103.2903 (2011).
  • Nizam, Hatice, and Saliha Sila Akin. 2014. “Sosyal Medyada Makine Öğrenmesi Ile Duygu Analizinde Dengeli ve Dengesiz Veri Setlerinin Performanslarının Karşılaştırılması.” IX. Türkiye'de İnternet Konferansı, 1-6.
  • Nasukawa, T. ve Yi, J. 2003. Sentiment analysis: capturing favorability using natural language processing, K-CAP 2003 Proceedings of the 2nd International Conference on Knowledge Capture, Sanibel Island, FL, 70-77.
  • O'Connor, B., R. Balasubramanyan, B. R. Routledge ve N. A. Smith. 2010. From tweets to polls: Linking text sentiment to public opinion time series. Tepper School of Business, 559.
  • O'leary, D. E. 2011. The use of social media in the supply chain: Survey and extensions. Intelligent Systems in Accounting, Finance and Management, 18(2-3): 121-144.
  • Özsert, Cüneyd Murad, and Arzucan Özgür. "Word polarity detection using a multilingual approach." In International Conference on Intelligent Text Processing and Computational Linguistics, pp. 75-82. Springer, Berlin, Heidelberg, 2013.
  • Öztürk, Nazan, and Serkan Ayvaz. 2018. “Sentiment Analysis on Twitter: A Text Mining Approach to the Syrian Refugee Crisis.” Telematics and Informatics 35 (1): 136–47.
  • Park, K., ve Ha, S. H. 2018. Mining user-generated opinions online with LDA model to discover service complaints. International Information Institute (Tokyo). Information, 21(3): 875-884.
  • Parlar, Tuba, and Selma Ayse Ozel. 2016. “A New Feature Selection Method for Sentiment Analysis of Turkish Reviews.” 2016 International Symposium on INnovations in Intelligent SysTems and Applications (INISTA). https://doi.org/10.1109/inista.2016.7571833.
  • Parlar, Tuba, Selma Ayşe Özel, and Fei Song. 2018. “QER: A New Feature Selection Method for Sentiment Analysis.” Human-Centric Computing and Information Sciences 8 (1). https://doi.org/10.1186/s13673-018-0135-8.
  • Sağlam, F. Otomatik Duygu Sözlüğü Geliştirilmesi ve Haberlerin Duygu Analizi. Doktora Tezi, Hacettepe Üniversitesi, Ankara (2019).
  • Santur, Yunus. 2019. “Sentiment Analysis Based on Gated Recurrent Unit.” 2019 International Artificial Intelligence and Data Processing Symposium (IDAP). https://doi.org/10.1109/idap.2019.8875985.
  • Seyfioğlu, Mehmet, and Mustafa Demirezen. 2017. “A Hierarchical Approach for Sentiment Analysis and Categorization of Turkish Written Customer Relationship Management Data.” In Proceedings of the 2017 Federated Conference on Computer Science and Information Systems. IEEE. https://doi.org/10.15439/2017f204.
  • Shehu, H. A., and S. Tokat. 2020. “A Hybrid Approach for the Sentiment Analysis of Turkish Twitter Data.” Artificial Intelligence and Applied Mathematics in Engineering Problems. https://doi.org/10.1007/978-3-030-36178-5_15.
  • Shehu, Harisu Abdullahi, Sezai Tokat, Md Haidar Sharif, and Sahin Uyaver. 2019. “Sentiment Analysis of Turkish Twitter Data.” AIP Conference Proceedings 2183 (1): 080004.
  • Taşlıoğlu, H.: Irony Detectıon On Turkısh Mıcroblog Texts, Yüksek Lisans Tezi, Orta-doğu Teknik Üniversitesi, Bilgisayar Mühendisliği, Ankara (2014).
  • Thelwall, Mike, Kevan Buckley, and Georgios Paltoglou. 2011. "Sentiment in Twitter events." Journal of the American Society for Information Science and Technology 62(2): 406-418.
  • Thelwall, M., Kevan Buckley, Georgios Paltoglou, D. Cai ve A. Kappas. 2010. Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61(12), 2544-2558.
  • Turkish WordNet: Ehsani R, Solak E, Yildiz OT. Constructing a WordNet for Turkish using manual and automatic annotation. ACM Transactions on Asian Language Information Processing 2018; 17 (3): 1-15. doi: 10.1145/3185664.
  • Turney P. Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: Proceedings of annual meeting of the Association for Computational Linguistics (ACL’02); 2002.
  • Türkmenoğlu, C. Türkçe Metinlerde Duygu Analizi, Yüksek Lisans Tezi, İstanbul Teknik Üniversitesi Fen Bilimleri Enstitüsü, İstanbul (2015).
  • Türkmenoglu, Cumali, and Ahmet Cüneyd Tantug. "Sentiment analysis in Turkish media." In International Conference on Machine Learning (ICML). 2014.
  • Ucan A, Naderalvojoud B, Sezer EA, Sever H. SentiWordNet for new language: automatic translation approach. In: 12th International Conference on Signal-Image Technology & Internet-Based Systems; Naples, Italy; 2016. pp. 308-315.
  • Ucan, A. "Automatic sentiment dictionary translation and using in sentiment analysis." Yükseklisans Tezi, Hacettepe Universitesi, Ankara, Turkey (2014).
  • Uslu, Abdullah, Sefa Tekin, and Tevfik Aytekin. 2019. Sentiment analysis in Turkish film comments. In 2019 27th Signal Processing and Communications Applications Conference (SIU). IEEE:1-4.
  • Wang, H., D. Can, A. Kazemzadeh, F. Bar ve S. Narayanan. 2012. A system for real-time twitter sentiment analysis of 2012 us presidential election cycle. In Proceedings of the ACL 2012 system demonstrations, 115-120.
  • Wright, A. 2009. Our sentiments, exactly, Communications of the ACM, 52 (4): 14-15.
  • Velioglu, Riza, Tugba Yildiz, and Savas Yildirim. 2018. “Sentiment Analysis Using Learning Approaches Over Emojis for Turkish Tweets.” 2018 3rd International Conference on Computer Science and Engineering (UBMK). https://doi.org/10.1109/ubmk.2018.8566260.
  • Vural, A. Gural, B. Barla Cambazoglu, Pinar Senkul, and Z. Ozge Tokgoz. "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, 2013.
  • Yurtalan, Gökhan, Murat Koyuncu, and Çi̇ğdem Turhan. 2019. “A Polarity Calculation Approach for Lexicon-Based Turkish Sentiment Analysis.” TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, March, 1325–39.

Sentiment Analysis in Turkish Texts

Yıl 2021, Cilt: 16 Sayı: 63, 1514 - 1534, 31.07.2021
https://doi.org/10.19168/jyasar.928843

Öz

Recently, the need for analyzing and understanding the story behind big data coming from increasing use of internet and social media becomes a trend and has gained a huge impact on businesses. Sentiment analysis is a method to analyze the opinion in a text in a systematic way that it defines the sentiment category and polarity in that text. The respected areas where sentiment analysis is applied is not only limited to linguistics, but also several applications in financial markets, marketing and social media analysis are observed in the literature. Although sentiment analysis can be applied in any language, English has a dominance in the literature because it is a globally spoken language. Therefore, sentiment analysis in Turkish texts still requires further attention as an open research area. In this research, we examined the relevant literature on sentiment analysis in Turkish texts in terms of frequently applied methodologies, open source libraries and databases. Consequently, we define research gaps and further research topics in application of sentiment analysis in Turkish texts.

Kaynakça

  • Açıkalın, Utku Umur, Benan Bardak, Mucahid Kutlu. 2020. Turkish Sentiment Analysis Using BERT. 28th Signal Processing and Communications Applications Conference (SIU). IEEE: 1-4.
  • Aguwa, C., M.H. Olya ve L. Monplaisir. 2017. Modeling of fuzzy-based voice of customer for business decision analytics. Knowledge-Based Systems: 125, 136-145.
  • Ahmetoğlu, Hüseyin, and Resul Daş. 2020. “Türkçe Otel Yorumlarıyla Eğitilen Kelime Vektörü Modellerinin Duygu Analizi Ile İncelenmesi.” Süleyman Demirel üniversitesi Fen Bilimleri Enstitüsü Dergisi, August, 455–63.
  • Akba, Firat, Alaettin Uçan, Ebru Akcapinar Sezer, and Hayri Sever. 2014. "Assessment of feature selection metrics for sentiment analyses: Turkish movie reviews". In 8th European Conference on Data Mining. 191(2002): 180-184.
  • Akba, F. Assesment of Feature Selection Metrics for Sentiment Analysis: Turkish Movie Reviews, Published Master's Thesis, Ankara, (2014).
  • Akgül, Eyüp Sercan, Caner Ertano, and Banu Diri. 2016. “Sentiment analysis with Twitter.” Pamukkale University Journal of Engineering Sciences 22 (2): 106–10.
  • Akın, Ahmet Afsin, ve Mehmet Dündar Akın. 2007. "Zemberek, an open source nlp framework for turkic languages." Structure 10: 1-5.
  • Alpkoçak, Adil, Mansur Alp Tocoglu, Azer Çelikten, and İrfan Aygün. 2019. “Türkçe Metinlerde Duygu Analizi Için Farklı Makine Öğrenmesi Yöntemlerinin Karşılaştırılması.” Deu Muhendislik Fakultesi Fen ve Muhendislik 21 (63): 719–25.
  • Arazy, O. ve Woo, C. 2007. Enhancing information retrieval through statistical natural language processing: a study of collocation indexing, MIS Quarterly, 31(3): 525-546.
  • Atan, Suat, ve Yetkin Çınar. 2019. Borsa İstanbul’da finansal haberler ile piyasa değeri ilişkisinin metin madenciliği ve duygu (sentiment) analizi ile incelenmesi. Ankara Üniversitesi SBF Dergisi, 74(1): 1-34.
  • Baker, M., ve J. Wurgler. 2007. Investor sentiment in the stock market. Journal of economic perspectives, 21(2), 129-152.
  • Bayraktar, Kivanc, Uraz Yavanoglu, and Alper Ozbilen. 2019. “A Rule-Based Holistic Approach for Turkish Aspect-Based Sentiment Analysis.” 2019 IEEE International Conference on Big Data (Big Data). https://doi.org/10.1109/bigdata47090.2019.9005473.
  • Bermingham, A., ve Alan F. S. 2010. Classifying sentiment in microblogs: is brevity an advantage?. In Proceedings of the 19th ACM international conference on Information and knowledge management, 1833-1836.
  • Bilgin, Metin ve İzzet Fatih Şentürk. 2017. Sentiment analysis on Twitter data with semi-supervised Doc2Vec. In 2017 international conference on computer science and engineering (UBMK).IEEE: 661-666.
  • Burcu, Akın, ve Şimşek, Umma, Tuğba, Gürsoy. 2018. Sosyal Medya Analitiği İle Değer Yaratma: Duygu Analizi İle Geleceğe Yönelim. Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 5(3): 797-811.
  • Catal, Cagatay, and Mehmet Nangir. 2017. “A Sentiment Classification Model Based on Multiple Classifiers.” Applied Soft Computing 50 (January): 135–41.
  • Ceron, A., L. Curini, S. Iacus ve G. Porro. 2014. Every tweet counts? How sentiment analysis of social media can improve our knowledge of citizens’ political preferences with an application to Italy and France. New media & society, 16(2): 340-358.
  • Ciftci, Basri, and Mehmet Serkan Apaydin. 2018. “A Deep Learning Approach to Sentiment Analysis in Turkish.” 2018 International Conference on Artificial Intelligence and Data Processing (IDAP). https://doi.org/10.1109/idap.2018.8620751.
  • Çalı, Sedef, and Şebnem Yılmaz Balaman. 2019. "Improved decisions for marketing, supply and purchasing: Mining big data through an integration of sentiment analysis and intuitionistic fuzzy multi criteria assessment." Computers & Industrial Engineering 129: 315-332.
  • Catal, Cagatay, and Mehmet Nangir. 2017. "A sentiment classification model based on multiple classifiers." Applied Soft Computing 50: 135-141.
  • Çeti̇n, Fatih Samet, and Gülşen Eryiğit. 2018. “Türkçe Hedef Tabanlı Duygu Analizi İçin Alt Görevlerin İncelenmesi – Hedef Terim, Hedef Kategori Ve Duygu Sınıfı Belirleme.” Bilişim Teknolojileri Dergisi, January. https://doi.org/10.17671/gazibtd.325865.
  • Çoban, Önder, Baris Özyer, and Gülsah Tümüklü Özyer. 2015. “Türkçe Twitter Mesajlarının Duygu Analizi Sentiment Analysis for Turkish Twitter Feeds.” In 2015 23nd Signal Processing and Communications Applications Conference (SIU). IEEE. 2388-2391.
  • Dave, K., Lawrence, S. ve Pennock, D.M. 2003, Mining the peanut gallery: opinion extraction and semantic classification of product reviews, Proceedings of the 12th International World Wide Web, ACM, Budapeşte.
  • Dehkharghani, Rahim, Berrin Yanikoglu, Yucel Saygin, and Kemal Oflazer. 2017. “Sentiment Analysis in Turkish at Different Granularity Levels.” Natural Language Engineering 23 (4): 535–59.
  • Dehkharghani, Rahim, Yucel Saygin, Berrin Yanikoglu, and Kemal Oflazer. 2016. "SentiTurkNet: a Turkish polarity lexicon for sentiment analysis." Language Resources and Evaluation 50, no. 3: 667-685.
  • Dehkharghani, Rahim. 2018. “A Hybrid Approach to Generating Adjective Polarity Lexicon and Its Application to Turkish Sentiment Analysis.” International Journal of Modern Education & Computer Science, 10(11). https://doi.org/10.5815/ijmecs.201.11.0.
  • Dehkharghani, Rahim, Sentiment Analysis in Turkish: Resources and Techniques, Doktora Tezi, Sabancı University, İstanbul (2015).
  • Demirci, Gozde Merve, Seref Recep Keskin, and Gulustan Dogan. 2019. “Sentiment Analysis in Turkish with Deep Learning.” 2019 IEEE International Conference on Big Data (Big Data). https://doi.org/10.1109/bigdata47090.2019.9006066.
  • Dülger, Oğuzhan. 2018. “Türkçe Metinlerde İroni Tespiti.” Proceedings of the 12th Turkish National Software Engineering Symposium (UYMS).
  • Erşahi̇n, Buket, Özlem Aktaş, Deniz Kilinç, and Mustafa Erşahi̇n. 2019. “A Hybrid Sentiment Analysis Method for Turkish.” TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 27 (3): 1780–93.
  • Fan, Z. P., Che, Y. J., ve Chen, Z. Y. 2017. Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis. Journal of Business Research 74: 90-100.
  • Farías, Delia Irazú Hernańdez, Viviana Patti, and Paolo Rosso. "Irony detection in twitter: The role of affective content." ACM Transactions on Internet Technology (TOIT) 16, no. 3 (2016): 1-24.
  • Farías, Irazú Hernández, José-Miguel Benedí, and Paolo Rosso. "Applying basic features from sentiment analysis for automatic irony detection." In Iberian Conference on Pattern Recognition and Image Analysis, pp. 337-344. Springer, Cham, 2015.
  • Gözükara, Furkan, and Selma Ayşe Özel. 2016. “An Experimental Investigation of Document Vector Computation Methods for Sentiment Analysis of Turkish and English Reviews.”
  • Hine, C. 2005, Virtual Methods: Issues in Social Research on the Internet, Berg, Oxford.
  • İskender, Elyase, and Gülgönül Bozoğlu Batı. 2015. “Comparing Turkish Universities Entrepreneurship and Innovativeness Index’s Rankings with Sentiment Analysis Results on Social Media.” Procedia - Social and Behavioral Sciences 195 (July): 1543–52.
  • Kama, Batuhan, Murat Ozturk, Pinar Karagoz, I. Hakki Toroslu, and Murat Kalender. 2017. “Analyzing Implicit Aspects and Aspect Dependent Sentiment Polarity for Aspect-Based Sentiment Analysis on Informal Turkish Texts.” In Proceedings of the 9th International Conference on Management of Digital EcoSystems. New York, NY, USA: ACM. https://doi.org/10.1145/3167020.3167041.
  • Kamisli Ozturk, Z., Z. İ. Erzurum Cicek, and Z. Ergul. 2017. “Sentiment Analysis: An Application to Anadolu University.” Acta Physica Polonica Series a 132 (3): 753–55. Karagoz, Pinar, Batuhan Kama, Murat Ozturk, I. Hakki Toroslu, and Deniz Canturk. 2019. “A Framework for Aspect Based Sentiment Analysis on Turkish Informal Texts.” Journal of Intelligent Information Systems 53 (3): 431–51.
  • Karamollaoğlu, Hamdullah, İbrahim Alper Doğru, Murat Dörterler, Anıl Utku, Oktay Yıldız. 2018. Sentiment analysis on Turkish social media shares through lexicon based approach. In 2018 3rd International Conference on Computer Science and Engineering. IEEE. 45-49.
  • Karaöz, Burcu, and U. Tuğba Gürsoy. 2018. “Adaptif Öğrenme Sözlüğü Temelli Duygu Analiz Algoritması Önerisi.” Bilişim Teknolojileri Dergisi, August. https://doi.org/10.17671/gazibtd.342419.
  • Karcioğlu, Abdullah Ammar ve Tolga Aydin. 2019. Sentiment analysis of Turkish and english twitter feeds using Word2Vec model. In 2019 27th Signal Processing and Communications Applications Conference (SIU). IEEE: 1-4.
  • Kaya, M., G. Fidan, and I. H. Toroslu. 2012. “Sentiment Analysis of Turkish Political News.” In 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, 1:174–80.
  • Kaynar, Oguz, Yasin Görmez, Mustafa Yildiz, and Ayşegül Albayrak. 2016. “Makine Öğrenmesi Yöntemleri Ile Duygu Analizi.” In International Artificial Intelligence and Data Processing Symposium (IDAP'16): 17-18.
  • Keleş, Pervan Nergis; 2017. “Sentiment Analysis Using a Random Forest Classifier on Turkish Web Comments.” Communications Faculty Of Science University of Ankara. https://doi.org/10.1501/commua1-2_0000000105.
  • Kozinets, R. 2009, Netnography: Doing Ethnographic Research Online, Sage Publications, Londra.
  • Ku, L.W., Ho, H.W. and Chen, H.H. 2009. Opinion mining and relationship discovery using CopeOpi opinion analysis system, Journal of the American Society for Information Science and Technology, 60 (7): 1486-1503.
  • Kumar, A., ve A. Jaiswal. 2020. Systematic literature review of sentiment analysis on Twitter using soft computing techniques. Concurrency and Computation: Practice and Experience, 32(1): e5107.
  • Liu, Bing. 2012. "Sentiment analysis and opinion mining." Synthesis lectures on human language technologies 5, no. 1: 1-167.
  • Medhat, Walaa, Ahmed Hassan, and Hoda Korashy. "Sentiment analysis algorithms and applications: A survey." Ain Shams engineering journal 5, no. 4 (2014): 1093-1113.
  • Metin, Senem Kumova, and Bahar Karaoğlan. "Türkiye Türkçesinde Eşdizimlerin İstatistiksel Yöntemlerle Belirlenmesi." Bilig 78 (2016): 253-286.
  • Misopoulos, F., Mitic, M., Kapoulas, A. and Karapiperis, C. 2014. "Uncovering customer service experiences with Twitter: the case of airline industry", Management Decision, 52 (4): 705-723.
  • Nalçakan, Yağiz, Şan Sitki Bayramoğlu, and Samed Tuna. 2015. “Sosyal Medya Verileri Üzerinde Yapay Öğrenme Ile Duygu Analizi Çalışması.” Technical Report. Trakya Üniversitesi.
  • Nielsen, Finn Årup. "A new ANEW: Evaluation of a word list for sentiment analysis in microblogs." arXiv preprint arXiv:1103.2903 (2011).
  • Nizam, Hatice, and Saliha Sila Akin. 2014. “Sosyal Medyada Makine Öğrenmesi Ile Duygu Analizinde Dengeli ve Dengesiz Veri Setlerinin Performanslarının Karşılaştırılması.” IX. Türkiye'de İnternet Konferansı, 1-6.
  • Nasukawa, T. ve Yi, J. 2003. Sentiment analysis: capturing favorability using natural language processing, K-CAP 2003 Proceedings of the 2nd International Conference on Knowledge Capture, Sanibel Island, FL, 70-77.
  • O'Connor, B., R. Balasubramanyan, B. R. Routledge ve N. A. Smith. 2010. From tweets to polls: Linking text sentiment to public opinion time series. Tepper School of Business, 559.
  • O'leary, D. E. 2011. The use of social media in the supply chain: Survey and extensions. Intelligent Systems in Accounting, Finance and Management, 18(2-3): 121-144.
  • Özsert, Cüneyd Murad, and Arzucan Özgür. "Word polarity detection using a multilingual approach." In International Conference on Intelligent Text Processing and Computational Linguistics, pp. 75-82. Springer, Berlin, Heidelberg, 2013.
  • Öztürk, Nazan, and Serkan Ayvaz. 2018. “Sentiment Analysis on Twitter: A Text Mining Approach to the Syrian Refugee Crisis.” Telematics and Informatics 35 (1): 136–47.
  • Park, K., ve Ha, S. H. 2018. Mining user-generated opinions online with LDA model to discover service complaints. International Information Institute (Tokyo). Information, 21(3): 875-884.
  • Parlar, Tuba, and Selma Ayse Ozel. 2016. “A New Feature Selection Method for Sentiment Analysis of Turkish Reviews.” 2016 International Symposium on INnovations in Intelligent SysTems and Applications (INISTA). https://doi.org/10.1109/inista.2016.7571833.
  • Parlar, Tuba, Selma Ayşe Özel, and Fei Song. 2018. “QER: A New Feature Selection Method for Sentiment Analysis.” Human-Centric Computing and Information Sciences 8 (1). https://doi.org/10.1186/s13673-018-0135-8.
  • Sağlam, F. Otomatik Duygu Sözlüğü Geliştirilmesi ve Haberlerin Duygu Analizi. Doktora Tezi, Hacettepe Üniversitesi, Ankara (2019).
  • Santur, Yunus. 2019. “Sentiment Analysis Based on Gated Recurrent Unit.” 2019 International Artificial Intelligence and Data Processing Symposium (IDAP). https://doi.org/10.1109/idap.2019.8875985.
  • Seyfioğlu, Mehmet, and Mustafa Demirezen. 2017. “A Hierarchical Approach for Sentiment Analysis and Categorization of Turkish Written Customer Relationship Management Data.” In Proceedings of the 2017 Federated Conference on Computer Science and Information Systems. IEEE. https://doi.org/10.15439/2017f204.
  • Shehu, H. A., and S. Tokat. 2020. “A Hybrid Approach for the Sentiment Analysis of Turkish Twitter Data.” Artificial Intelligence and Applied Mathematics in Engineering Problems. https://doi.org/10.1007/978-3-030-36178-5_15.
  • Shehu, Harisu Abdullahi, Sezai Tokat, Md Haidar Sharif, and Sahin Uyaver. 2019. “Sentiment Analysis of Turkish Twitter Data.” AIP Conference Proceedings 2183 (1): 080004.
  • Taşlıoğlu, H.: Irony Detectıon On Turkısh Mıcroblog Texts, Yüksek Lisans Tezi, Orta-doğu Teknik Üniversitesi, Bilgisayar Mühendisliği, Ankara (2014).
  • Thelwall, Mike, Kevan Buckley, and Georgios Paltoglou. 2011. "Sentiment in Twitter events." Journal of the American Society for Information Science and Technology 62(2): 406-418.
  • Thelwall, M., Kevan Buckley, Georgios Paltoglou, D. Cai ve A. Kappas. 2010. Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61(12), 2544-2558.
  • Turkish WordNet: Ehsani R, Solak E, Yildiz OT. Constructing a WordNet for Turkish using manual and automatic annotation. ACM Transactions on Asian Language Information Processing 2018; 17 (3): 1-15. doi: 10.1145/3185664.
  • Turney P. Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: Proceedings of annual meeting of the Association for Computational Linguistics (ACL’02); 2002.
  • Türkmenoğlu, C. Türkçe Metinlerde Duygu Analizi, Yüksek Lisans Tezi, İstanbul Teknik Üniversitesi Fen Bilimleri Enstitüsü, İstanbul (2015).
  • Türkmenoglu, Cumali, and Ahmet Cüneyd Tantug. "Sentiment analysis in Turkish media." In International Conference on Machine Learning (ICML). 2014.
  • Ucan A, Naderalvojoud B, Sezer EA, Sever H. SentiWordNet for new language: automatic translation approach. In: 12th International Conference on Signal-Image Technology & Internet-Based Systems; Naples, Italy; 2016. pp. 308-315.
  • Ucan, A. "Automatic sentiment dictionary translation and using in sentiment analysis." Yükseklisans Tezi, Hacettepe Universitesi, Ankara, Turkey (2014).
  • Uslu, Abdullah, Sefa Tekin, and Tevfik Aytekin. 2019. Sentiment analysis in Turkish film comments. In 2019 27th Signal Processing and Communications Applications Conference (SIU). IEEE:1-4.
  • Wang, H., D. Can, A. Kazemzadeh, F. Bar ve S. Narayanan. 2012. A system for real-time twitter sentiment analysis of 2012 us presidential election cycle. In Proceedings of the ACL 2012 system demonstrations, 115-120.
  • Wright, A. 2009. Our sentiments, exactly, Communications of the ACM, 52 (4): 14-15.
  • Velioglu, Riza, Tugba Yildiz, and Savas Yildirim. 2018. “Sentiment Analysis Using Learning Approaches Over Emojis for Turkish Tweets.” 2018 3rd International Conference on Computer Science and Engineering (UBMK). https://doi.org/10.1109/ubmk.2018.8566260.
  • Vural, A. Gural, B. Barla Cambazoglu, Pinar Senkul, and Z. Ozge Tokgoz. "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, 2013.
  • Yurtalan, Gökhan, Murat Koyuncu, and Çi̇ğdem Turhan. 2019. “A Polarity Calculation Approach for Lexicon-Based Turkish Sentiment Analysis.” TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, March, 1325–39.
Toplam 82 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

Sinem Tokcaer 0000-0001-8842-3574

Yayımlanma Tarihi 31 Temmuz 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 16 Sayı: 63

Kaynak Göster

APA Tokcaer, S. (2021). Türkçe Metinlerde Duygu Analizi. Yaşar Üniversitesi E-Dergisi, 16(63), 1514-1534. https://doi.org/10.19168/jyasar.928843
AMA Tokcaer S. Türkçe Metinlerde Duygu Analizi. Yaşar Üniversitesi E-Dergisi. Temmuz 2021;16(63):1514-1534. doi:10.19168/jyasar.928843
Chicago Tokcaer, Sinem. “Türkçe Metinlerde Duygu Analizi”. Yaşar Üniversitesi E-Dergisi 16, sy. 63 (Temmuz 2021): 1514-34. https://doi.org/10.19168/jyasar.928843.
EndNote Tokcaer S (01 Temmuz 2021) Türkçe Metinlerde Duygu Analizi. Yaşar Üniversitesi E-Dergisi 16 63 1514–1534.
IEEE S. Tokcaer, “Türkçe Metinlerde Duygu Analizi”, Yaşar Üniversitesi E-Dergisi, c. 16, sy. 63, ss. 1514–1534, 2021, doi: 10.19168/jyasar.928843.
ISNAD Tokcaer, Sinem. “Türkçe Metinlerde Duygu Analizi”. Yaşar Üniversitesi E-Dergisi 16/63 (Temmuz 2021), 1514-1534. https://doi.org/10.19168/jyasar.928843.
JAMA Tokcaer S. Türkçe Metinlerde Duygu Analizi. Yaşar Üniversitesi E-Dergisi. 2021;16:1514–1534.
MLA Tokcaer, Sinem. “Türkçe Metinlerde Duygu Analizi”. Yaşar Üniversitesi E-Dergisi, c. 16, sy. 63, 2021, ss. 1514-3, doi:10.19168/jyasar.928843.
Vancouver Tokcaer S. Türkçe Metinlerde Duygu Analizi. Yaşar Üniversitesi E-Dergisi. 2021;16(63):1514-3.