Abstract
“Bilgi çağı” ya da “dijital
çağ” olarak adlandırılan 21. yüzyılda hayatımızın her alanında kullandığımız
veri, elektronik olarak toplanabilmekte, işlenebilmekte, analiz edilip
kullanılabilmektedir. Digital veriler sosyal ağlardan, kullandığımız araçlardan
(Nesnelerin İnterneti), kamera sistemleri ve OCR sitemleri gibi günlük hayatta
kullandığımız bilgileri digital bilgiye çeviren pek çok araç tarafından elde
edilebilmektedir. Günümüzde çığ gibi büyüyen büyük verinin analiz edilmesi ve
veriyi bilgiye dönüştürecek faydalı kalıpların bulunması önemli bir konudur. Bu
çalışmada “Mutluluk” ve “Hüzün” gibi iki temel insan duygusu cinsiyet durumu da
dikkate alınarak, Google n-gram derleminden faydalanılarak analiz edilmiştir.
Bu derlem, 1500 ve 2008 yılları arasında yayınlanan milyonlarca kitap taranarak
elde edilmiştir. İnsanların milyonlarca kitapta kullandığı kelimelerden oluşan
bu derlem, insana özgü özellik ve davranışlar için bir gösterge olarak
düşünülebilir. Bu çalışma, insan duygularının, duygularına karşılık gelen
kelimelerin sıklığıyla tahmin edilebileceği hipotezine dayanmaktadır.
Makalemizde, gelecek yıllardaki “Mutluluk” ve “Hüzün” duygularının kullanım
sıklığını cinsiyet kırılımına göre tahmin etmek için regresyon analiz
yöntemleri kullanılmaktadır. Bu çalışma “Google n-gram Veritabanı ile Üzüntü ve
Mutluluk Üzerine Duygu Analizi”çalışmasının cinsiyet kırılımını içeren
genişletilmiş halidir.
Keywords
Duygu çıkarımı cinsiyete göre üzüntü ve mutluluk analizi duygu tahmini Google n-grams duygu madenciliği
References
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Abstract
References
- [1]. Michel, J. B et all, (2011). The Google Books Team, 176-182.
- [2]. Michel, J. B. et all, (2011). Quantitative analysis of culture using millions of digitized books. science, 331(6014), 176-182.
- [3]. Smallwood, C., (2015). The complete guide to using Google in libraries: instruction, administration, and staff productivity (Vol. 1). Rowman & Littlefield.
- [4]. Wang, H., Prendinger, H., & Igarashi, T. (2004, April). Communicating emotions in online chat using physiological sensors and animated text. In CHI'04 extended abstracts on Human factors in computing systems (pp. 1171-1174). ACM.
- [5]. Hunter, P. G., Schellenberg, E. G., & Schimmack, U. (2010). Feelings and perceptions of happiness and sadness induced by music: Similarities, differences, and mixed emotions. Psychology of Aesthetics, Creativity, and the Arts, 4(1), 47.
- [6]. Liu, Y., Sourina, O., & Nguyen, M. K. (2011). Real-time EEG-based emotion recognition and its applications. In Transactions on computational science XII (pp. 256-277). Springer, Berlin, Heidelberg.
- [7]. Bond, A., & Lader, M. (1974). The use of analogue scales in rating subjective feelings. British Journal of Medical Psychology, 47(3), 211-218.
- [8]. Zhe, X., & Boucouvalas, A. C. (2002, July). Text-to-emotion engine for real time internet communication. In Proceedings of International Symposium on Communication Systems, Networks and DSPs (pp. 164-168).
- [9]. Hancock, J. T., Landrigan, C., & Silver, C. (2007, April). Expressing emotion in text-based communication. In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 929-932). ACM.
- [10]. Kamvar, S. D., & Harris, J. (2011, February). We feel fine and searching the emotional web. In Proceedings of the fourth ACM international conference on Web search and data mining (pp. 117-126). ACM.
- [11]. Kaur, A., & Gupta, V. (2013). A survey on sentiment analysis and opinion mining techniques. Journal of Emerging Technologies in Web Intelligence, 5(4), 367-371.
- [12]. Klein, M., & Nelson, M. L. (2009, April). Correlation of term count and document frequency for Google n-grams. In European Conference on Information Retrieval (pp. 620-627). Springer, Berlin, Heidelberg.
- [13]. Pauls, A., & Klein, D. (2011, June). Faster and smaller n-gram language models. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1 pp. 258-267.
- [14]. Islam, A., Milios, E., & Keselj, V. (2012). Comparing Word Relatedness Measures Based on Google n grams. Proceedings of COLING 2012: Posters, 495-506.
- [15]. Islam, A., Milios, E., & Kešelj, V. (2012, May). Text similarity using google tri-grams. In Canadian Conference on Artificial Intelligence (pp. 312-317). Springer, Berlin, Heidelberg.
- [16]. Juola, P. (2013). Using the Google N-Gram corpus to measure cultural complexity. Literary and linguistic computing, 28(4), 668-675.
- [17]. Joubarne, C., & Inkpen, D. (2011, May). Comparison of semantic similarity for different languages using the Google N-gram corpus and second-order co-occurrence measures. In Canadian Conference on Artificial Intelligence (pp. 216-221). Springer, Berlin, Heidelberg.
- [18]. Davies, M. (2014). Making Google Books n-grams useful for a wide range of research on language change. International Journal of Corpus Linguistics, 19(3), 401-416.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Engineering |
| Journal Section | Makaleler(Araştırma) |
| Authors | |
| Publication Date | June 1, 2019 |
| Published in Issue | Year 2019 Volume: 12 Issue: 1 |
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