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Ürün Özelliklerinin Konu Modelleme Yöntemi ile Çıkartılması

Year 2016, Volume: 9 Issue: 1, 51 - 58, 07.06.2017

Abstract



Özellik tabanlı duygu analizindeki en önemli ve güç görevlerden biri duyguların ifade edildiği başlık olarak tanımlanan özelliklerin çıkartılmasıdır. İnternetin günlük hayatımızın vazgeçilmez bir parçası haline gelmesi ile birlikte çevrimiçi kullanıcı yorumlarında yaşanan büyük artış, hem otomatik bir yöntemin geliştirilmesi hem de özelliklerin doğru bir şekilde çıkartılmasını gerektirmektedir. Son yıllarda metin madenciliği uygulamalarında büyük önem kazanan konu modelleme yöntemleri ise bu alanda tercih edilmeye başlanmıştır. Büyük boyutlu dokümanlardan denetimsiz bir şekilde gizli yapıyı keşfeden konu modelleme güçlü bir yöntem olarak karşımıza çıkmaktadır. Bu çalışmada kullanıcı yorumlarından ürün özelliklerini çıkarmada en popüler konu modelleme yöntemlerinden biri olan Gizli Dirichlet Ayırımı (GDA) kullanılmıştır. Türkçe otel yorumları üzerinden elde edilen deneysel sonuçlar, GDA'nın özellik çıkarmada başarılı olduğunu göstermiştir.


References

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  • [2] Thet, T. T., Na, J. C., Khoo, C. S. G. Aspect-based sentiment analysis of movie reviews on discussion boards, Journal of Information Science, 36 (6), 2010, pp. 823-848.
  • [3] Ha, S.H., Bae, S.Y., Son, L.K. Impact of Online Consumer Reviews on Product Sales: Quantitative Analysis of the Source Effect, Applied Mathematics & Information Sciences, 9(2L), 2015, pp. 373-387.
  • [4] Li, Z., Jing, F., Zhu, X. Movie Review Mining and Summarization, In Proceedings of the 15th ACM International Conference on Information and Knowledge Management (CIKM’06), 2006, pp. 43-50.
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  • [7] Li, F., Huang, M., Zhu, X. Sentiment Analysis with Global Topics and Local Dependency, In Proceedings of the 24th AAAI Conference on Artificial Intelligence, 2010, pp. 1371-1376.
  • [8] Wang, W. Sentiment Analysis of Online Product Reviews with Semi-supervised Topic Sentiment Mixture Model, In Proceedings of 7th International Conference on Fuzzy Systems and Knowledge Discovery, 2010, pp. 2385-2389.
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  • [11] Xianghua, F., Guo, L., Yanyan, G., Zhiqiang, W. Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet Lexicon, Knowledge-Based Systems, 37, 2013, pp. 186-195.
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  • [14] Wang, T., Cai, Y., Leung, H., Lau, R.Y.K., Li, Q., Min, H. Product aspect extraction supervised with online domain knowledge, Knowledge-Based Systems, 71, 2014, pp. 86-100.
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  • [16] Lau, R.Y.K., Li, C., Liao, S.S.Y. Social analytics: Learning fuzzy product ontologies for aspect-oriented sentiment analysis, Decision Support Systems, 65, 2014, pp. 80-94.
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  • [19] Poria, S., Chaturvedi, I., Cambria, E., Bisio, F. Sentic LDA: Improving on LDA with Semantic Similarity for Aspect-Based Sentiment Analysis, IJCNN, 2016.
  • [20] Stevyers, M., Griffiths, T. Probabilistic Topic Models, Handbook of Latent Semantic Analysis. Editör: Landauer, T., McNamara, D.S. , Dennis, S., Kintsch W. Erlbaum, 2007.
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  • [22] Mei, Q., Shen, X., Zhai, C. Automatic Labeling of Multinomial Topic Models, In Proceedings of ACM KDD, 2007, pp. 490-499.
  • [23] Phan, X-H., Nguyen, C-T., Le, D-T., Nguyen, L-M., Horiguchi, S., Ha, Q-T. A Hidden Topic-Based Framework toward Building Applications with Short Web Documents, IEEE Transactions on Knowledge and Data Engineering, 23(7), 2011, pp. 961-976.
  • [24] Jadhav N. Topic Models for Sentiment analysis: A Literature Survey, Teknik Rapor, 2014, pp. 1-11.
  • [25] Bishop, C. M. Pattern Recognition and Machine Learning, Editör: Jordan, M., Kleinberg, J., Schölköpf B. Springer, 2006.
  • [26] Ekinci, E., Türkmen, H., İlhan Omurca, S. Multi-word Aspect Extraction from User Reviews, 6th World Conference on Innovatıon and Computer Science (INSODE-2016), 2016.
  • [27] Türkmen, H., Ekinci, E., İlhan Omurca, S. A Novel Method for Extracting Feature Opinion Pairs for Turkish Lecture Notes in Artificial Intelligence Springer, ISBN: 978-3-319-44747-6, 2016, pp. 162-171.
  • [28] Akın, M. D., Akın, A. A. Türk Dilleri için Açık Kaynaklı Doğal Dil İşleme Kütüphanesi : Zemberek, Elektrik Mühendisliği, 431, 2007 pp. 38-44.
  • [29] Blei, D.M., Ng, A.Y., Jordan, M.I. Latent dirichlet allocation, The Journal of Machine Learning Research, 3, 2004. pp. 993-1022.
Year 2016, Volume: 9 Issue: 1, 51 - 58, 07.06.2017

Abstract

References

  • [1] Picazo-Vela, S., Chou, S.Y., Melcher, A.J., Pearson, J.M. Why provide an online review? An extended theory of planned behavior and the role of Big-Five personality traits, Computers in Human Behavior, 26(4), 2010, pp. 685-696.
  • [2] Thet, T. T., Na, J. C., Khoo, C. S. G. Aspect-based sentiment analysis of movie reviews on discussion boards, Journal of Information Science, 36 (6), 2010, pp. 823-848.
  • [3] Ha, S.H., Bae, S.Y., Son, L.K. Impact of Online Consumer Reviews on Product Sales: Quantitative Analysis of the Source Effect, Applied Mathematics & Information Sciences, 9(2L), 2015, pp. 373-387.
  • [4] Li, Z., Jing, F., Zhu, X. Movie Review Mining and Summarization, In Proceedings of the 15th ACM International Conference on Information and Knowledge Management (CIKM’06), 2006, pp. 43-50.
  • [5] Liu, B. Sentiment Analysis and Opinion Mining Synthesis Lectures on Human Language Technologies, Editör: Hirst, G. Morgan & Claypool, 2012.
  • [6] Türkmen, H., İlhan Omurca, S., Ekinci, E. An Aspect Based Sentiment Analysis on Turkish Hotel Reviews, Girne American University Journal of Social and Applied Sciences, 6, 2016, pp. 9-15.
  • [7] Li, F., Huang, M., Zhu, X. Sentiment Analysis with Global Topics and Local Dependency, In Proceedings of the 24th AAAI Conference on Artificial Intelligence, 2010, pp. 1371-1376.
  • [8] Wang, W. Sentiment Analysis of Online Product Reviews with Semi-supervised Topic Sentiment Mixture Model, In Proceedings of 7th International Conference on Fuzzy Systems and Knowledge Discovery, 2010, pp. 2385-2389.
  • [9] Jo, Y., Oh, A. Aspect and Sentiment Unification Model for Online Review Analysis, In Proceedings of 4th ACM International Conference on Web Search and Data Mining, 2011, pp. 815-824.
  • [10] Xueke, X., Xueki, C., Songbo, T., Yue, L., Huawei, S. Aspect-Level Opinion Mining of Online Customer Reviews, China Communications, 10(3), 2013, pp. 25-41.
  • [11] Xianghua, F., Guo, L., Yanyan, G., Zhiqiang, W. Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet Lexicon, Knowledge-Based Systems, 37, 2013, pp. 186-195.
  • [12] Ding, W., Song, X., Guo, L., Xiong, Z., Hu, X. A Novel Hybrid HDP-LDA Model for Sentiment Analysis, In Proceedings of 2013 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, 2013, pp. 329-336.
  • [13] Moghaddam, S., Ester, M. The flda model for aspect-based opinion mining: addressing the cold start problem, In Proceedings of the 22nd International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2013, pp. 909–918.
  • [14] Wang, T., Cai, Y., Leung, H., Lau, R.Y.K., Li, Q., Min, H. Product aspect extraction supervised with online domain knowledge, Knowledge-Based Systems, 71, 2014, pp. 86-100.
  • [15] Zheng, X., Lin, Z., Wang, X., Lin, K.J., Song, M. Incorporating appraisal expression patterns into topic modeling for aspect and sentiment word identification, Knowledge-Based Systems, 61, 2014, pp. 29-47.
  • [16] Lau, R.Y.K., Li, C., Liao, S.S.Y. Social analytics: Learning fuzzy product ontologies for aspect-oriented sentiment analysis, Decision Support Systems, 65, 2014, pp. 80-94.
  • [17] Yin, S., Han, J., Huang, Y., Kumar, K. Dependency-Topic-Affects-Sentiment-LDA Model for Sentiment Analysis, In Proceedings of 2014 IEEE 26th International conference on Tools with Artificial Intelligence, 2014, pp. 413-418.
  • [18] Bagheri, A., Saraee, M., Jong, F. Care more about customers: Unsupervised domain-independent aspect detection for sentiment analysis of customer reviews, Knowledge-Based Systems, 52, 2013, pp. 201–213.
  • [19] Poria, S., Chaturvedi, I., Cambria, E., Bisio, F. Sentic LDA: Improving on LDA with Semantic Similarity for Aspect-Based Sentiment Analysis, IJCNN, 2016.
  • [20] Stevyers, M., Griffiths, T. Probabilistic Topic Models, Handbook of Latent Semantic Analysis. Editör: Landauer, T., McNamara, D.S. , Dennis, S., Kintsch W. Erlbaum, 2007.
  • [21] Blei, D. M. Probabilistic Topic Models, Communications of the ACM, 55(4), 2012, pp. 77-84.
  • [22] Mei, Q., Shen, X., Zhai, C. Automatic Labeling of Multinomial Topic Models, In Proceedings of ACM KDD, 2007, pp. 490-499.
  • [23] Phan, X-H., Nguyen, C-T., Le, D-T., Nguyen, L-M., Horiguchi, S., Ha, Q-T. A Hidden Topic-Based Framework toward Building Applications with Short Web Documents, IEEE Transactions on Knowledge and Data Engineering, 23(7), 2011, pp. 961-976.
  • [24] Jadhav N. Topic Models for Sentiment analysis: A Literature Survey, Teknik Rapor, 2014, pp. 1-11.
  • [25] Bishop, C. M. Pattern Recognition and Machine Learning, Editör: Jordan, M., Kleinberg, J., Schölköpf B. Springer, 2006.
  • [26] Ekinci, E., Türkmen, H., İlhan Omurca, S. Multi-word Aspect Extraction from User Reviews, 6th World Conference on Innovatıon and Computer Science (INSODE-2016), 2016.
  • [27] Türkmen, H., Ekinci, E., İlhan Omurca, S. A Novel Method for Extracting Feature Opinion Pairs for Turkish Lecture Notes in Artificial Intelligence Springer, ISBN: 978-3-319-44747-6, 2016, pp. 162-171.
  • [28] Akın, M. D., Akın, A. A. Türk Dilleri için Açık Kaynaklı Doğal Dil İşleme Kütüphanesi : Zemberek, Elektrik Mühendisliği, 431, 2007 pp. 38-44.
  • [29] Blei, D.M., Ng, A.Y., Jordan, M.I. Latent dirichlet allocation, The Journal of Machine Learning Research, 3, 2004. pp. 993-1022.
There are 29 citations in total.

Details

Subjects Engineering
Journal Section Makaleler(Araştırma)
Authors

Ekin Ekinci

Sevinç İlhan Omurca

Publication Date June 7, 2017
Published in Issue Year 2016 Volume: 9 Issue: 1

Cite

APA Ekinci, E., & İlhan Omurca, S. (2017). Ürün Özelliklerinin Konu Modelleme Yöntemi ile Çıkartılması. Türkiye Bilişim Vakfı Bilgisayar Bilimleri Ve Mühendisliği Dergisi, 9(1), 51-58.
AMA Ekinci E, İlhan Omurca S. Ürün Özelliklerinin Konu Modelleme Yöntemi ile Çıkartılması. TBV-BBMD. June 2017;9(1):51-58.
Chicago Ekinci, Ekin, and Sevinç İlhan Omurca. “Ürün Özelliklerinin Konu Modelleme Yöntemi Ile Çıkartılması”. Türkiye Bilişim Vakfı Bilgisayar Bilimleri Ve Mühendisliği Dergisi 9, no. 1 (June 2017): 51-58.
EndNote Ekinci E, İlhan Omurca S (June 1, 2017) Ürün Özelliklerinin Konu Modelleme Yöntemi ile Çıkartılması. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi 9 1 51–58.
IEEE E. Ekinci and S. İlhan Omurca, “Ürün Özelliklerinin Konu Modelleme Yöntemi ile Çıkartılması”, TBV-BBMD, vol. 9, no. 1, pp. 51–58, 2017.
ISNAD Ekinci, Ekin - İlhan Omurca, Sevinç. “Ürün Özelliklerinin Konu Modelleme Yöntemi Ile Çıkartılması”. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi 9/1 (June 2017), 51-58.
JAMA Ekinci E, İlhan Omurca S. Ürün Özelliklerinin Konu Modelleme Yöntemi ile Çıkartılması. TBV-BBMD. 2017;9:51–58.
MLA Ekinci, Ekin and Sevinç İlhan Omurca. “Ürün Özelliklerinin Konu Modelleme Yöntemi Ile Çıkartılması”. Türkiye Bilişim Vakfı Bilgisayar Bilimleri Ve Mühendisliği Dergisi, vol. 9, no. 1, 2017, pp. 51-58.
Vancouver Ekinci E, İlhan Omurca S. Ürün Özelliklerinin Konu Modelleme Yöntemi ile Çıkartılması. TBV-BBMD. 2017;9(1):51-8.

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