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Metin Duygu sınıflandırılmasında hibrit wavelet yönteminin kullanımı

Yıl 2021, Cilt: 36 Sayı: 2, 701 - 714, 05.03.2021

İlknur Dönmez Zafer Aslan

https://doi.org/10.17341/gazimmfd.701313
Cited By: 1

Öz

Verilerin her geçen gün arttığı günümüzde herhangi bir metnin anlamsal ve duygusal çözümlemesi ihtiyaç duyulan konulardan biridir. Çalışmamız metinlerin sınıflandırılmasında kullanılabilecek üst anlam ilişkilerini çıkarmak ve metinlerin duygu sınıflandırmasını yapmak için yeni bir yöntem önermektedir. Bu yöntem daha önce metin analizinde çok az kullanılmış dalgacık dönüşüm yöntemidir. Çalışmamızda bu yöntemin klasik sınıflandırma algoritmaları ile birleştirilirmiş hali kullanılmaktadır. Dalgacık dönüşüm yöntemi metin içindeki anahtar kelimelerin üst anlamlarını ve temsil ettikleri ağırlıkları bulmaya yardım etmektedir. Duygu sınıflandırması probleminde, klasik yöntemler ile birlikte metin anahtar kelime vektörleri üzerinde dalgacık dönüşümü yapıldıktan sonra bulunan ağırlıkların kullanılması doğrulukları artırmıştır.

Anahtar Kelimeler

Wavelet duygu analizi metin duygu görselleştirmesi

Destekleyen Kurum

Yok

Proje Numarası

Yok

Teşekkür

Yazarlar, Wavelet uygulamaları konusundaki araştırmalarımıza sağladığı önemli katkılar ve desteklerden dolayı ISIAM (Hindistan Endüstri ve Uygulamalı Matematik Topluluğu) Başkanı olan ve maalesef 20 Ocak 2020 tarihinde aramızdan ayrılan Prof, Dr, Abul Hassan SIDDIQI'yı saygıyla anarlar ve çalışmalarını Prof, Dr, Abul Hassan SIDDIQI’ya ithaf ederler.

Kaynakça

  • [1] Akansu, A. N., & Smith, M. J. (Eds.). Subband and wavelet transforms: design and applications (Vol. 340). Springer Science & Business Media,2012
  • [2] Meyer, Y., & Ryan, R. D. Wavelets: Algorithms and Applications. SIAM, Philadelphia, PA, 1993.
  • [3] Chan, Y. T. (1994). Wavelet basics. Springer Science & Business Media.
  • [4] Strang, G., Nguyen, T. Wavelets and Filter Banks,Wellesley-Cambridge Press, ISBN 0-9614088-7-1 Box 812060, Wellesley MA 02181 USA, 1996.
  • [5] Daubechies, I. Ten Lectures on Wavelets, Society for Industrial and Applied Mathematics, Capital City Press, Philadelphia, Pennsylvania, 1992
  • [6] Daubechies. I. The wavelet transform, timefrequency localization and signal analysis. IEEE Trans. Inf. Theor., 36(5):961–1005, September, 2006
  • [7] Farkov, Y. A., P. Manchanda, A. H. Siddiqi, (2019): Construction of Wavelets Through Walsh Functions, ISBN - 978 -981-13-6370-2 (e-book), pp. 382.
  • [8] Polikar, R. The story of wavelets. Physics and modern topics in mechanical and electrical engineering, 192-197, 1999
  • [9] Siddiqi, A. H., Manchanda, P., & Kocvara, M. (2002, July). Fast wavelet-based algorithms for option pricing. In Proc. world Multi conference on Systemic, Cybernetics and Informatics.
  • [10] Li, T., Li, Q., Zhu, S., & Ogihara, M. A survey on wavelet applications in data mining. ACM SIGKDD Explorations Newsletter, 4(2), 49-68, 2002.
  • [11] Aggarwal, C. C. On effective classification of strings with wavelets. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 163-172), July, 2002.
  • [12] Xu, C., & Zhou, Y. M. Wavelet-based hierarchical document categorization. In 2007 International Conference on Wavelet Analysis and Pattern Recognition (Vol. 4, pp. 1524-1527). IEEE, November 2007.
  • [13] Xexéo, G., de Souza, J., Castro, P. F., & Pinheiro, W. A. Using wavelets to classify documents. In 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (Vol. 1, pp. 272-278). IEEE, December 2008.
  • [14] Mahajan, A., Jat, S., & Roy, S. (2015, July). Feature Selection for Short Text Classification using Wavelet Packet Transform. In Proceedings of the Nineteenth Conference on Computational Natural Language Learning (pp. 321-326).
  • [15] Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of computational science, 2(1), 1-8.
  • [16] Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational linguistics, 37(2), 267-307.
  • [17] Cambria, E., Schuller, B., Xia, Y., & Havasi, C. (2013). New avenues in opinion mining and sentiment analysis. IEEE Intelligent systems, 28(2), 15-21.
  • [18] Thelwall, M., Buckley, K., & Paltoglou, G. (2012). Sentiment strength detection for the social web. Journal of the American Society for Information Science and Technology, 63(1), 163-173.
  • [19] Kilimci, Z. H. (2020). Financial Sentiment Analysis with Deep Community Models for Stock Market (DCM). Journal of the Faculty of Engineering and Architecture of Gazi University, 35(2), 635-650.
  • [20] Çoban, Ö., Özyer, B., & Özyer, G. T. (2015, May). Sentiment analysis for Turkish Twitter feeds. In 2015 23nd Signal Processing and Communications Applications Conference (SIU) (pp. 2388-2391). IEEE.
  • [21] Chidambarathanu, K., & Shunmuganathan, K. L. (2017). Predicting user preferences on changing trends and innovations using SVM based sentiment analysis. Cluster Computing, 1-5.
  • [22] Zhang, W., Kong, S. X., & Zhu, Y. C. (2019). Sentiment classification and computing for online reviews by a hybrid SVM and LSA based approach. Cluster Computing, 22(5), 12619-12632.
  • [23] Akhtar, M. S., Kumar, A., Ghosal, D., Ekbal, A., & Bhattacharyya, P. (2017, September). A multilayer perceptron based ensemble technique for fine-grained financial sentiment analysis. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (pp. 540-546).
  • [24] Alboaneen, D. A., Tianfield, H., & Zhang, Y. (2017, December). Sentiment analysis via multi-layer perceptron trained by meta-heuristic optimisation. In 2017 IEEE International Conference on Big Data (Big Data) (pp. 4630-4635). IEEE.
  • [25] Jotheeswaran, J., & Koteeswaran, S. (2015). Decision tree based feature selection and multilayer perceptron for sentiment analysis. Journal of Engineering and Applied Sciences, 10(14), 5883-5894.
  • [26] Taddy, M. (2013). Multinomial inverse regression for text analysis. Journal of the American Statistical Association, 108(503), 755-770.
  • [27] You, Q., Luo, J., Jin, H., & Yang, J. (2016, February). Cross-modality consistent regression for joint visual-textual sentiment analysis of social multimedia. In Proceedings of the Ninth ACM international conference on Web search and data mining (pp. 13-22).
  • [28] Zhang, Z., Zou, Y., & Gan, C. (2018). Textual sentiment analysis via three different attention convolutional neural networks and cross-modality consistent regression. Neurocomputing, 275, 1407-1415.
  • [29] Jijkoun, V., de Rijke, M., & Weerkamp, W. (2010) Generating focused topic-specific sentiment lexicons. In Proceedings of the 48th annual meeting of the association for computational linguistics (pp. 585-594). Association for Computational Linguistics.
  • [30] Felix G, Surya K, Hagen M, and Sebastian Z. (2018) Aspect-Based Sentiment Analysis of Drug Reviews Applying Cross-Domain and Cross-Data Learning. In Proceedings of the 2018 International Conference on Digital Health (DH '18). ACM, New York, NY, USA, 121-125, 2018.
  • [31] UCI Machine Learning Repository, (2020), Harsha Nagesh and Sanjay Goil and Alok N. Choudhary. Adaptive Grids for Clustering Massive Data Sets. Department of Energy ASCI, https://archive.ics.uci.edu/ml/datasets/Movie
  • [32] Thoomkuzhy, A. M., (2020). Drug Reviews: Cross-condition and Cross-source Analysis by Review Quantification Using Regional CNN-LSTM Models.

Document Sentiment Classification Using Hybrid Wavelet Methodologies

Yıl 2021, Cilt: 36 Sayı: 2, 701 - 714, 05.03.2021

İlknur Dönmez Zafer Aslan

https://doi.org/10.17341/gazimmfd.701313
Cited By: 1

Öz

Proje Numarası

Yok

Kaynakça

  • [1] Akansu, A. N., & Smith, M. J. (Eds.). Subband and wavelet transforms: design and applications (Vol. 340). Springer Science & Business Media,2012
  • [2] Meyer, Y., & Ryan, R. D. Wavelets: Algorithms and Applications. SIAM, Philadelphia, PA, 1993.
  • [3] Chan, Y. T. (1994). Wavelet basics. Springer Science & Business Media.
  • [4] Strang, G., Nguyen, T. Wavelets and Filter Banks,Wellesley-Cambridge Press, ISBN 0-9614088-7-1 Box 812060, Wellesley MA 02181 USA, 1996.
  • [5] Daubechies, I. Ten Lectures on Wavelets, Society for Industrial and Applied Mathematics, Capital City Press, Philadelphia, Pennsylvania, 1992
  • [6] Daubechies. I. The wavelet transform, timefrequency localization and signal analysis. IEEE Trans. Inf. Theor., 36(5):961–1005, September, 2006
  • [7] Farkov, Y. A., P. Manchanda, A. H. Siddiqi, (2019): Construction of Wavelets Through Walsh Functions, ISBN - 978 -981-13-6370-2 (e-book), pp. 382.
  • [8] Polikar, R. The story of wavelets. Physics and modern topics in mechanical and electrical engineering, 192-197, 1999
  • [9] Siddiqi, A. H., Manchanda, P., & Kocvara, M. (2002, July). Fast wavelet-based algorithms for option pricing. In Proc. world Multi conference on Systemic, Cybernetics and Informatics.
  • [10] Li, T., Li, Q., Zhu, S., & Ogihara, M. A survey on wavelet applications in data mining. ACM SIGKDD Explorations Newsletter, 4(2), 49-68, 2002.
  • [11] Aggarwal, C. C. On effective classification of strings with wavelets. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 163-172), July, 2002.
  • [12] Xu, C., & Zhou, Y. M. Wavelet-based hierarchical document categorization. In 2007 International Conference on Wavelet Analysis and Pattern Recognition (Vol. 4, pp. 1524-1527). IEEE, November 2007.
  • [13] Xexéo, G., de Souza, J., Castro, P. F., & Pinheiro, W. A. Using wavelets to classify documents. In 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (Vol. 1, pp. 272-278). IEEE, December 2008.
  • [14] Mahajan, A., Jat, S., & Roy, S. (2015, July). Feature Selection for Short Text Classification using Wavelet Packet Transform. In Proceedings of the Nineteenth Conference on Computational Natural Language Learning (pp. 321-326).
  • [15] Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of computational science, 2(1), 1-8.
  • [16] Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational linguistics, 37(2), 267-307.
  • [17] Cambria, E., Schuller, B., Xia, Y., & Havasi, C. (2013). New avenues in opinion mining and sentiment analysis. IEEE Intelligent systems, 28(2), 15-21.
  • [18] Thelwall, M., Buckley, K., & Paltoglou, G. (2012). Sentiment strength detection for the social web. Journal of the American Society for Information Science and Technology, 63(1), 163-173.
  • [19] Kilimci, Z. H. (2020). Financial Sentiment Analysis with Deep Community Models for Stock Market (DCM). Journal of the Faculty of Engineering and Architecture of Gazi University, 35(2), 635-650.
  • [20] Çoban, Ö., Özyer, B., & Özyer, G. T. (2015, May). Sentiment analysis for Turkish Twitter feeds. In 2015 23nd Signal Processing and Communications Applications Conference (SIU) (pp. 2388-2391). IEEE.
  • [21] Chidambarathanu, K., & Shunmuganathan, K. L. (2017). Predicting user preferences on changing trends and innovations using SVM based sentiment analysis. Cluster Computing, 1-5.
  • [22] Zhang, W., Kong, S. X., & Zhu, Y. C. (2019). Sentiment classification and computing for online reviews by a hybrid SVM and LSA based approach. Cluster Computing, 22(5), 12619-12632.
  • [23] Akhtar, M. S., Kumar, A., Ghosal, D., Ekbal, A., & Bhattacharyya, P. (2017, September). A multilayer perceptron based ensemble technique for fine-grained financial sentiment analysis. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (pp. 540-546).
  • [24] Alboaneen, D. A., Tianfield, H., & Zhang, Y. (2017, December). Sentiment analysis via multi-layer perceptron trained by meta-heuristic optimisation. In 2017 IEEE International Conference on Big Data (Big Data) (pp. 4630-4635). IEEE.
  • [25] Jotheeswaran, J., & Koteeswaran, S. (2015). Decision tree based feature selection and multilayer perceptron for sentiment analysis. Journal of Engineering and Applied Sciences, 10(14), 5883-5894.
  • [26] Taddy, M. (2013). Multinomial inverse regression for text analysis. Journal of the American Statistical Association, 108(503), 755-770.
  • [27] You, Q., Luo, J., Jin, H., & Yang, J. (2016, February). Cross-modality consistent regression for joint visual-textual sentiment analysis of social multimedia. In Proceedings of the Ninth ACM international conference on Web search and data mining (pp. 13-22).
  • [28] Zhang, Z., Zou, Y., & Gan, C. (2018). Textual sentiment analysis via three different attention convolutional neural networks and cross-modality consistent regression. Neurocomputing, 275, 1407-1415.
  • [29] Jijkoun, V., de Rijke, M., & Weerkamp, W. (2010) Generating focused topic-specific sentiment lexicons. In Proceedings of the 48th annual meeting of the association for computational linguistics (pp. 585-594). Association for Computational Linguistics.
  • [30] Felix G, Surya K, Hagen M, and Sebastian Z. (2018) Aspect-Based Sentiment Analysis of Drug Reviews Applying Cross-Domain and Cross-Data Learning. In Proceedings of the 2018 International Conference on Digital Health (DH '18). ACM, New York, NY, USA, 121-125, 2018.
  • [31] UCI Machine Learning Repository, (2020), Harsha Nagesh and Sanjay Goil and Alok N. Choudhary. Adaptive Grids for Clustering Massive Data Sets. Department of Energy ASCI, https://archive.ics.uci.edu/ml/datasets/Movie
  • [32] Thoomkuzhy, A. M., (2020). Drug Reviews: Cross-condition and Cross-source Analysis by Review Quantification Using Regional CNN-LSTM Models.
Toplam 32 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

İlknur Dönmez 0000-0002-8344-1180

Zafer Aslan 0000-0001-7707-7370

Proje Numarası Yok
Yayımlanma Tarihi 5 Mart 2021
Gönderilme Tarihi 9 Mart 2020
Kabul Tarihi 11 Ekim 2020
Yayımlandığı Sayı Yıl 2021 Cilt: 36 Sayı: 2

Kaynak Göster

APA Dönmez, İ., & Aslan, Z. (2021). Metin Duygu sınıflandırılmasında hibrit wavelet yönteminin kullanımı. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 36(2), 701-714. https://doi.org/10.17341/gazimmfd.701313
AMA Dönmez İ, Aslan Z. Metin Duygu sınıflandırılmasında hibrit wavelet yönteminin kullanımı. GUMMFD. Mart 2021;36(2):701-714. doi:10.17341/gazimmfd.701313
Chicago Dönmez, İlknur, ve Zafer Aslan. “Metin Duygu sınıflandırılmasında Hibrit Wavelet yönteminin kullanımı”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 36, sy. 2 (Mart 2021): 701-14. https://doi.org/10.17341/gazimmfd.701313.
EndNote Dönmez İ, Aslan Z (01 Mart 2021) Metin Duygu sınıflandırılmasında hibrit wavelet yönteminin kullanımı. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 36 2 701–714.
IEEE İ. Dönmez ve Z. Aslan, “Metin Duygu sınıflandırılmasında hibrit wavelet yönteminin kullanımı”, GUMMFD, c. 36, sy. 2, ss. 701–714, 2021, doi: 10.17341/gazimmfd.701313.
ISNAD Dönmez, İlknur - Aslan, Zafer. “Metin Duygu sınıflandırılmasında Hibrit Wavelet yönteminin kullanımı”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 36/2 (Mart 2021), 701-714. https://doi.org/10.17341/gazimmfd.701313.
JAMA Dönmez İ, Aslan Z. Metin Duygu sınıflandırılmasında hibrit wavelet yönteminin kullanımı. GUMMFD. 2021;36:701–714.
MLA Dönmez, İlknur ve Zafer Aslan. “Metin Duygu sınıflandırılmasında Hibrit Wavelet yönteminin kullanımı”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, c. 36, sy. 2, 2021, ss. 701-14, doi:10.17341/gazimmfd.701313.
Vancouver Dönmez İ, Aslan Z. Metin Duygu sınıflandırılmasında hibrit wavelet yönteminin kullanımı. GUMMFD. 2021;36(2):701-14.

Cited By

Twitter’da COVID-19 aşılarına karşı kamu duyarlılığının çoğunluk oylama sınıflandırıcısı temelli makine öğrenmesi ile duygu analizi
Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi
https://doi.org/10.17341/gazimmfd.1030198