Kelime Vektörü Yöntemlerinin Model Oluşturma Sürelerinin Karşılaştırılması

Yıl 2019, , 141 - 146, 30.04.2019

Metin Bilgin

https://doi.org/10.17671/gazibtd.472226

Cited By: 3

Öz

Bu çalışmada duygu analizi için oluşturulan iki farklı veri kümesi, kelime vektörü algoritması olan Word2Vec ile modellenmiştir. Model oluşturulurken Word2Vec’in iki farklı yöntemi olan CBoW (Continous Bag of Words) ve Skip-Gram kullanılmıştır. Word2Vec ile bir metnin modelini oluşturmak için genellikle Ortalama yöntemi kullanılmaktadır. Bu çalışmada hem CBoW hem de Skip-Gram yöntemleriyle bir metni modellemek için üç farklı yöntem önerilmiştir. Model oluşturma (eğitim zamanı) süreleri her ikisi içinde ölçülmüştür. Sonuç olarak modelleme süresi açısından CBoW’un Skip-Gram’dan daha başarılı olduğu deneysel olarak gösterilmiştir.

Anahtar Kelimeler

Word2Vec, Model Süresi, Eğitim süresi, CBOW, Skip-Gram, doğal dil işleme

Comparison of Modeling Time of Word Vector Methods

Öz

In this study, two different datasets for sentiment analysis have been modeled by Word2Vec that it is a word vector algorithm. While the model is creating that has used two different methods CBoW and Skip-Gram of Word2Vec. Generally, the arithmetic mean is used for modeling a text with Word2Vec. In this study, three different methods for modeling a text are suggested on both CBoW and Skip-Gram. Its modeling time (training time) is measured.  As a result, it was experimentally shown that CBoW is more successful than Skip-Gram based for modeling time.

Anahtar Kelimeler

Word2Vec, modeling time, training time, CBoW, Skip-Gram, natural language processing

Kaynakça

• [1] D. Zhang, H. Xu, Z. Su, Y. Xu, “Chinese comments sentiment classification based on word2vec and SVMperf”, Expert Systems with Applications, 42(4), 1857-1863, 2015.
• [2] B. Dickinson, W. Hu, “Sentiment analysis of investor opinions on Twitter”, Social Networking, 4(03), 62-71, 2015.
• [3] D. Tang, F. Wei, N. Yang, M. Zhou, T. Liu, B. Qin, “Learning sentiment-specific word embedding for twitter sentiment classification”, 52nd Annual Meeting of the Association for Computational Linguistics, Baltimore, Maryland, USA,1555-1565, 23-25 Haziran, 2014.
• [4] J. Polpinij, N. Srikanjanapert, P. Sopon, “Word2Vec Approach for Sentiment Classification Relating to Hotel Reviews”, International Conference on Computing and Information Technology, Bangkok, Thailand, 308-316, 6-7 Temmuz, 2017.
• [5] G. Şahin, “Turkish document classification based on Word2Vec and SVM classifier”, Signal Processing and Communications Applications Conference, Antalya, Turkey,1-4,15-18 Mayıs, 2017.
• [6] B. Xue, C. Fu, Z. Shaobin, “A study on sentiment computing and classification of sina weibo with word2vec”, International Congress on Big Data, Anchorage, AK, USA, 358-363, 27 Haziran-2 Temmuz, 2014.
• [7] M. Bilgin., İ.F. Şentürk, “Sentiment analysis on Twitter data with semi-supervised Doc2Vec”, International Conference on Computer Science and Engineering, Antalya, Turkey, 661-666, 5-8 Ekim, 2017.
• [8] M. Bilgin, H. Köktaş, “Word2Vec Based Sentiment Analysis for Turkish Texts”, International Conference on Engineering Technologies, Konya, Turkey, 106-109, 26-28 Ekim, 2017.
• [9] A. El Mahdaouy, E. Gaussier, S. O. El Alaoui, “Arabic Text Classification Based on Word and Document Embeddings”, International Conference on Advanced Intelligent Systems and Informatics, Cairo, Eygpt, 32-41, 24-26 Ekim, 2016.
• [10] Ö. Çoban, I. Karabey, “Music genre classification with word and document vectors”, Signal Processing and Communications Applications Conference, Antalya, Turkey, 1-4, 15-18 Mayıs, 2017.
• [11] H. Ma, X. Wiang, J. Hou, Y. Lu, “Course recommendation based on semantic similarity analysis”, International Conference on Control Science and Systems Engineering, Beijing, China, 638-641, 17-19 Ağustos, 2017.
• [12] M. Razzaghnoori, S. Hedieh, K.J. Iman, “Question classification in Persian using word vectors and frequencies”, Cognitive Systems Research, 47, 16-27, 2018.
• [13] H. Polat, M. Körpe, “TBMM Genel Kurul Tutanaklarından Yakın Anlamlı Kavramların Çıkarılması”, Bilişim Teknolojileri Dergisi, 11(3), 235-244, 2018.
• [14] T. Mikolov, K. Chen, G. Corrado, J. Dean, “Efficient Estimation of Word Representations in Vector Space”, International Conference on Learning Representations, Arizona, USA, 1-12, 2-4 Mayıs, 2013.
• [15] Internet: X Data Set, https:// www.dropbox.com/s/aji68llxmtcuu5l/data.zip, 05.09.2018.
• [16] Internet: Y Data Set, https://www.kaggle.com/c/si650winter11/data, 05.09.2018
• [17] M.R. Berthold, N. Cebron, F. Dill, T.R. Gabriel, T. Kötter, T. Meinl, P. Ohl, K. Thiel, B. Wiswedel, “KNIME-the Konstanz information miner: version 2.0 and beyond”, AcM SIGKDD explorations Newsletter, 11(1), 26-31, 2009.
• [18] Internet: Mean, https://www.wikipedia.com, 08.09.2018.
• [19] Internet: Mean-Median, https://byjus.com/mean-median-mode-formula, 08.09.2018.