Türkçe bilgisayarlı dil bilimi çalışmalarında his analizi

Bilgisayarlı dil bilimi; sözlü ya da yazılı dili anlamayı, matematiksel olarak ifade etmeyi hedefleyen ve bu hedefe ulaşmak için yöntemler, modeller ve araçlar öneren disiplinler arası bir bilim dalıdır. Bilgisayarlı dil bilimi çalışmalarının bir araştırma alanı olan his analizi; ses, görüntü ya da metin içerisinde hangi hislerin ne oranda yer aldığını bulma işlemine verilen addır. İnternetin yaygınlaşması, sayısal içeriğin çoğalması, saklama ve hesaplama gücünün artması gibi gelişmeler hem otomatik his analizi yapmanın önünü açmış hem de his analizini bir gereklilik hâline getirmiştir. Metinlerde his analizi konusunu Türk dili özelinde özetleyen bu çalışma, öncelikle his analizinin tarihçesi ve önemini dil bilim bakış açısıyla açıklamayı hedeflemekte ve his analizinin güncel uygulama alanlarından kısaca bahsetmektedir.

Anahtar Kelimeler:

Bilgisayarlı dil bilimi, metinlerde his analizi, dil bilimi

Emotion analysis in Turkish computational linguistics studies

Computational linguistics is an interdisciplinary field that aims to understand the verbal or written language, to express it mathematically and suggest methods, models and tools to achieve these goals. Emotion analysis, a research area of computational linguistics; is the process of finding which feelings taking place in what proportion in sound, image or text data. The developments such as the proliferation of the Internet, the increase of digital content, the increase of storage and computing power have both paved the way for automatic emotion analysis and made emotion analysis an important need. This study, which summarizes the subject of emotion analysis, aims to explain the history and importance of emotion analysis from a linguistic perspective, and briefly introduce the current application areas of emotion analysis.

Keywords:

Computational linguistics, emotion analysis in texts,

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Abdaoui, A., Azé, J., Bringay, S. ve Poncelet, P. (2017). FEEL: a French expanded emotion lexicon. Language Resources and Evaluation, 51(3), 833–855. doi:10.1007/s10579-016-9364-5
Abdul-Mageed, M., Alhuzli, H., Elhija, D. A., Diab, M. ve Duaa’Abu Elhija, M. D. (2016). DINA: A multidialect dataset for arabic emotion analysis. The 2nd workshop on Arabic corpora and processing tools içinde (s. 29).
Aggarwal, C. C. ve Zhai, C. (2012). A survey of text classification algorithms. Mining text data içinde (ss. 163–222). Springer.
Akba, F., Uçan, A., Sezer, E. ve Sever, H. (2014). Assessment of feature selection metrics for sentiment analyses: Turkish movie reviews. 8th European Conference on Data Mining 2014, 191 (2002), 180–184.
Akın, A. A. ve Akın, M. D. (2007). Zemberek, an open source Nlp framework for Turkic Languages. Structure, 10, 1–5. doi:10.1.1.556.69
Alm, C. O., Roth, D. ve Sproat, R. (2005). Emotions from text: machine learning for text-based emotion prediction. Proceedings of the conference on human language technology and empirical methods in natural language processing içinde (ss. 579–586).
Aman, S. ve Szpakowicz, S. (2007). Identifying expressions of emotion in text. International Conference on Text, Speech and Dialogue içinde (ss. 196–205).
Bai, X. (2011). Predicting consumer sentiments from online text. Decision Support Systems, 50(4), 732–742.
Bandhakavi, A., Wiratunga, N., Padmanabhan, D. ve Massie, S. (2017). Lexicon based feature extraction for emotion text classification. Pattern Recognition Letters, 93, 133–142. doi:10.1016/j.patrec.2016.12.009
Boynukalin, Z. ve Karagoz, P. (2013). Emotion analysis on Turkish texts. Information Sciences and Systems 2013 içinde (ss. 159–168). Springer.
Briciu, A. ve Lupea, M. (2017). RoEmoLex - a Romanian emotion lexicon. Studia Universitatis Babeș-Bolyai Informatica, 62(2), 45–56. doi:10.24193/subbi. 2017.2.04
Demirci, S. (2014). Emotion analysis on Turkish tweets. Middle East Technical University.
Dwi Prasetyo, N. ve Hauff, C. (2015). Twitter-based election prediction in the developing world. Proceedings of the 26th ACM Conference on Hypertext & Social Media içinde (ss. 149–158).
Ekman, P. (1972). Universals and cultural differences in facial expressions of emotion. Nebraska Symposium on Motivation içinde (C 19, ss. 207–282). Ekman, P. (1992). An argument for basic emotions. Cognition & emotion, 6(3–4), 169–200.
Ekman, P. ve Friesen, W. V. (1976). Measuring facial movement. Environmental Psychology and Nonverbal Behavior, 1(1), 56–75. doi:10.1007/BF01115465
Eryigit, G. (2014). ITU Turkish NLP web service. Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics içinde (ss. 1–4).
Hakkani-Tür, D. Z., Oflazer, K. ve Tür, G. (2002). Statistical morphological disambiguation for agglutinative languages. Computers and the Humanities, 36(4), 381–410.
Hochreiter, S. ve Schmidhuber, J. J. (1997). Long short-term memory. Neural Computation, 9(8), 1–32.
Ilgen, B., Adali, E. ve Tantug, A. C. (2016). Exploring feature sets for Turkish word sense disambiguation. Turkish Journal of Electrical Engineering & Computer Sciences, 24(5), 4391–4405.
Mikolov, T., Corrado, G., Chen, K., Dean, J., Corrado, G. ve Dean, J. (2013).
Efficient estimation of word representations in vector space. Proceedings of the International Conference on Learning Representations (ICLR 2013), 1–12.
Mohammad, S. M. ve Bravo-Marquez, F. (2017). Emotion intensities in tweets. SEM 2017: The Sixth Joint Conference on Lexical and Computational Semantics içinde (ss. 65–77).
Mohammad, S. M. ve Turney, P. D. (2013). Crowdsourcing a word-emotion association lexicon. Computational Intelligence, 29(3), 436–465. doi:10.1111/ j.1467-8640.2012.00460.x
Naderalvojoud, B., Ucan, A. ve Akcapinar Sezer, E. (2018). HUMIR at IEST- 2018: Lexicon-sensitive and left-right context-sensitive bi-lstm for implicit emotion recognition. Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis içinde (ss. 182–188). Association for Computational Linguistics.
Oflazer, K. (1994). Two-level description of Turkish morphology. Literary and linguistic computing, 9(2), 137–148.
Oflazer, K. ve Saraçlar, M. (2018). Turkish natural language processing. Springer.
Pang, B., Lee, L. ve Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10 içinde (ss. 79– 86). Association for Computational Linguistics. doi:10.3115/1118693.1118704 Plutchik, R. (1991). The emotions. University Press of America.
Smailovic, J., Grcar, M., Lavrac, N. ve Znidarsic, M. (2013). Predictive sentiment analysis of tweets: A stock market application. International Workshop on Human- Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data içinde (ss. 77–88).
Tocoglu, M. A. ve Alpkocak, A. (2018). TREMO: A dataset for emotion analysis in Turkish. Journal of Information Science, 44(6), 848–860.
Tocoglu, M. A. ve Alpkocak, A. (2019). Lexicon-based emotion analysis in Turkish. Turkish Journal Of Electrical Engineering & Computer Sciences, 27(2), 1213–1227.
Tocoglu, M. A., Ozturkmenoglu, O. ve Alpkocak, A. (2019). Emotion analysis from Turkish tweets using deep neural networks. IEEE Access, 7, 183061– 183069. doi:10.1109/ACCESS.2019.2960113
Turney, P. D. (2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. Proceedings of the 40th Annual Meeting on Association for Computational Linguistics içinde (ss. 417–424). Association for Computational Linguistics. doi:10.3115/1073083.1073153
Wallbott, H. G. ve Scherer, K. R. (1986). How universal and specific is emotional experience? Evidence from 27 countries on five continents. Information (International Social Science Council), 25(4), 763–795.
Wiebe, J., Wilson, T. ve Cardie, C. (2005). Annotating expressions of opinions and emotions in language. Language resources and evaluation, 39(2–3), 165–210.