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A Survey On Sentiment Analysis And Opinion Mining, Methods And Approaches

Yıl 2018, Cilt: 6 Sayı: 4, 668 - 693, 01.12.2018

Barış Özyurt Muhammet Ali Akcayol

https://doi.org/10.15317/Scitech.2018.160

Öz

In recent years, with the widespread usage of Web applications, the platforms where people express their opinions and ideas are continously increasing. There are too much text data containing people’s ideas in these platforms. Manual analysis and classification of these text data is not possible, so there is need to automatically analyze and classify them. So opinion mining and sentiment analysis works have been popular in recent years. In this article, opinion mining and sentiment analysis subject is comprehensively described with details. Also the works done in the literature about this subject are comprehensively analyzed and presented in the article as literature survey.

Anahtar Kelimeler

Sentiment analysis Opinion mining

Kaynakça

  • Akba, F., Uçan, A., Sezer, E.A., Sever, H., “Assessment of Feature Selection Metrics for Sentiment Analyses: Turkish Movie Reviews”, 8th European Conference on Data Mining, Lizbon, Portekiz, 180-184, 2014.
  • Akbaş, E., 2012, Aspect Based Opinion Mining on Turkish Tweets, Yüksek Lisans Tezi, Bilkent Üniversitesi, Mühendislik ve Fen Bilimleri Enstitüsü, Ankara.
  • Akhtar, M.S., Gupta, D., Ekbal, A., Bhattacharyya, P., 2017, “Feature Selection and Ensemble Construction: A Two-Step Method for Aspect Based Sentiment Analysis”, Knowledge-Based Systems, Cilt 125, ss.116-135.
  • Aytekin, Ç., 2013, “An Opinion Mining Task in Turkish Language A Model for Assigning Opinions in Turkish Blogs to the Polarities”, Journalism and Mass Communication, Cilt 3, ss.179-198.
  • Bagheria, A., Saraee, M., Jong, F., 2013, “Care More About Customers: Unsupervised Domain-Independent Aspect Detection for Sentiment Analysis of Customer Reviews”, Knowledge-Based Systems, Cilt 52, ss.201-203.
  • Balahur, A., Hermida, J.M., Montoyo, A., 2012, “Detecting Implicit Expressions of Emotion in Text: A Comparative Analysis”, Decision Support Systems, Cilt 53, ss.742-753.
  • Baccianella, S., Esuli, A., Sebastiani, F. “SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining”, In Proceedings of the 7th Conference on Language Resources and Evaluation (LREC'10), Valletta, MT, İtalya, 2200–2204, 2010.
  • Bespalov, D., Bai, B., Qi, Y., Shokoufandeh, A., “Sentiment Classification Based on Supervised Latent n-gram Analysis”, In Proceedings of CIKM '11, 20th ACM International Conference on Information and Knowledge Management, Glasgow, İngiltere, 375-382, 2011.
  • Bhadane, C., Dalal, H., Doshi, H., 2015, “Sentiment Analysis: Measuring Opinions”, Procedia Computer Science, Cilt 45, ss.808-814.
  • Blei, D.M., Ng, A.Y., Jordan, M.I., 2003, “Latent Dirichlet Allocation”, The Journal of Machine Learning Research, Cilt 3, ss.993-1022.
  • Brody, S., Elhadad, N., “An Unsupervised Aspect-Sentiment Model for Online Reviews”, Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, LA, ABD, 804-812, 2010.
  • Can, Ü., Alataş, B., 2017, “Duygu Analizi ve Fikir Madenciliği Algoritmalarının İncelenmesi”, International Journal of Pure and Applied Sciences, Cilt 3, ss.75-111.
  • Cesa-Bianchi, N., Gentile, C., Zaniboni, L., 2006, “Incremental Algorithms for Hierarchical Classification”, Journal of Machine Learning Research, Cilt 7, ss.31-54.
  • Chen, T., Xu, R., He, Y., Wang, X., 2017, “Improving Sentiment Analysis via Sentence Type Classification using BiLSTM-CRF and CNN”, Expert Systems with Applications, Cilt 72, ss.221-230.
  • Choi, Y., Cardie, C., “Learning with Compositional Semantics as Structural Inference for Subsentential Sentiment Analysis”, In Proceedings of EMNLP'08, Conference on Empirical Methods in Natural Language Processing, Waikiki, Hawaii, 793-801, 2008.
  • Church, K.W., Hanks, P., 1990, “Word Association Norms, Mutual İnformation and Lexicography”, Computational Linguistics, Cilt 16, ss.22-29.
  • Çetin, M., Amasyalı, M.F., “Supervised and Traditional Term Weighting Methods for Sentiment Analysis”, Signal Processing and Communications Applications Conference (SIU), Haspolat, KKTC, 1-4, 2013.
  • Dalkılıç, F.E., Gelişli, S., Diri B., “Named Entity Recognition from Turkish texts”, IEEE 18. Sinyal İşleme ve İletişim Uygulamaları Kurultayı, Diyarbakır, Türkiye, ss. 918-920, 2010.
  • Davidov, D., Tsur, O., Rappoport, A., “Enhanced Sentiment Learning Using Twitter Hashtags and Smileys”, In Proceedings of COLING'10, 23rd International Conference on Computational Linguistics, Pekin, Çin, 241-249, 2010.
  • Dehkharghani, R., Saygin, Y., Yanikoglu, B., Oflazer, K., 2016, “Sentiment Analysis in Turkish at Different Granularity Levels”, Natural Language Engineering, Cilt 23.
  • Dehkharghani, R., Saygin, Y., Yanikoglu, B., Oflazer, K., 2016, “SentiTurkNet: a Turkish Polarity Lexicon for Sentiment Analysis”, Language Resources and Evaluation, Cilt: 50, ss.667-685.
  • Ding, X., Liu, B., Yu, PS., “A Holistic Lexicon-Based Approach to Opinion Mining”, In Proceedings of WSDM-2008, Conference on Web Search and Web Data Mining, Stanford ABD, 231-240, 2008.
  • Dong, Z., Dong, Q., 2006, Hownet And the Computation of Meaning, World Scientific Publishing Co. Eirinaki, M., Pisal, S., Singh, J., 2012, “Feature-Based Opinion Mining and Ranking”, Journal of Computer and System Sciences, Cilt 78, ss.1175-1184.
  • Ekici, E., Somurca, S, İ., 2016, “Ürün Özelliklerinin Konu Modelleme Yöntemi ile Çıkarılması”, Türkiye Bilişim Vakfı Bilgisayar Bilimleri Ve Mühendisliği Dergisi, Cilt: 9, ss.51-58.
  • Eroğul, U., 2009, Sentiment Analysis In Turkish, Yüksek Lisans Tezi, Orta Doğu Teknik Üniversitesi, Fen Bilimleri Enstitüsü, Ankara.
  • Eryiğit, G., “ITU Turkish NLP Web Service.”, In proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics, Göteborg, İsviçre, 1-4, 2014.
  • Fernández-Gavilanes, M., Álvarez-López, T., Juncal-Martínez, J., Costa-Montenegro, E., González-Castaño, F.J., 2016, “Unsupervised Method for Sentiment Analysis in Online Texts”, Expert Systems with Applications, Cilt 58, ss.57-75.
  • Frank E., Hall M. A., Witten I. H., 2016. The WEKA Workbench. Online Appendix for "Data Mining: Practical Machine Learning Tools and Techniques", Morgan Kaufmann, 4. Baskı.
  • Ganter, B., Wille, R., 1999, Formal Concept Analysis, Mathematical Foundation, Springer-Verlag Berlin Heidelberg.
  • Goldensohn, S.B., Hannan, K., McDonald, R., Neylon, T., Reis, G.A., Reynar, J., “Building a Sentiment Summarizer for Local Service Reviews”, In Proceedings of WWW’08 Workshop: NLP in the Information Explosion Era, Pekin, Çin, 2008.
  • Habernal, I., Ptacek, T., Steinberger, J., 2015, “Supervised Sentiment Analysis in Czech Social Media”, Information Processing & Management, Cilt 50, ss.693-707.
  • Hailong, Z., Wenyan, G., Bo, J., “Machine Learning and Lexicon Based Methods for Sentiment Classification: A Survey”, WISA'14 Proceedings of the 2014 11th Web Information System and Application Conference, Tianjin, China, 262-265, 2014.
  • Hu, M., Liu, B., “Mining and Summarizing Customer Reviews”, KDD’04, International Conference on Knowledge Discovery and Data Mining, Seattle, ABD, 168-177, 2004.
  • IMDb, http://www.imdb.com, Ziyaret Tarihi: 01.10.2017.
  • ISEAR Databank, http://emotion-research.net/toolbox/toolboxdatabase.2006-10-13.2581092615, Ziyaret Tarihi: 01.10.2017.
  • Isidro, P.M., Rafael, V.G., Francisco, G.S., “Ontology-Guided Approach to Feature-Based Opinion Mining”, NLDB’11, Proceedings of the 16th international conference on Natural language processing and information systems, Alicante, İspanya, 193-200, 2011.
  • Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T., “Target Dependent Twitter Sentiment Classification”, In Proceedings of ACL‘11, 49th Annual Meeting of the Association for Computational Linguistics, Portland, ABD, 151-160, 2011.
  • Kang, D., Park, Y., 2014, “Review-based Measurement of Customer Satisfaction in Mobile Service: Sentiment Analysis and VIKOR Approach”, Expert Systems with Applications, Cilt 41, ss.1041-1050.
  • Kang, H., Yoo, SJ., Han, D., 2012, “Senti-lexicon and Improved Naïve Bayes Algorithms for Sentiment Analysis of Restaurant Reviews”, Expert Systems with Applications, Cilt 39, ss.6000-6010.
  • Kansal, H., Toshniwal, D., 2014, “Aspect based Summarization of Context Dependent Opinion Words”, Procedia Computer Science, Cilt 35, ss.166-175.
  • Kaya, M., Fidan, G., Toroslu, I.H., “Sentiment Analysis of Turkish Political News”, In Proceedings of WI-IAT’12 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology, Macau, Çin, 174-180, 2012.
  • Kennedy, A., Inkpen, D., 2006, “Sentiment Classification of Movie Reviews Using Contextual Valence Shifters”, Computational Intelligence, Cilt 22, ss.110-125.
  • Khan, F.H., Qamar, U., Bashir, S., 2016, “eSAP - A decision support framework for enhanced sentiment analysis and polarity classification”, Information Sciences, Cilt 367-368, ss.862-873.
  • Kontopoulos, E., Berberidis, C., Dergiades, T., Bassiliades, N., 2013, “Ontology-based sentiment Analysis of Twitter Posts”, Expert Systems with Applications, Cilt 40, ss.4065-4074.
  • Kushal, D., Steve, L., Pennock, D.M., “Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews”, In Proceedings of WWW’03, 12th International Conference on World Wide Web, Budapest Congress Centre, Macaristan, ss. 519-528, 2003.
  • Küçük, D., Yazıcı, A., “Named Entity Recognition Experiments on Turkish Texts”, In Proceedings of FQAS-2009, 8th International Conference on Flexible Query Answering Systems, Roskilde, Danimarka, ss. 524-535, 2009.
  • Lafferty, J., McCallum, A., Pereira F., “Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data”, In Proceedings of ICML '01, 18th International Conference on Machine Learning, Williams College, Williamstown, MA, ABD, ss. 282–289, 2001.
  • Li, S., Zhou, L., Li, Y., 2015, “Improving Aspect Extraction by Augmenting a Frequency-Based Method with Web-Based Similarity Measures”, Information Processing and Management, Cilt 51, ss.58-67.
  • Liao, C., Fengn, C., Yang, S., Huang, H., 2016, “Topic-related Chinese Message Sentiment Analysis”, Neurocomputing, Cilt 210, ss.237-246.
  • Liu, B., 2012, Sentiment Analysis and Opinion Mining, Morgan & Claypool Publishers.
  • Liu, Q., Gao, Z., Liu, B., Zhang, Y., 2016, “Automated Rule Selection for Opinion Target Extraction”, Knowledge-Based Systems, Cilt 104, ss.74-88.
  • Maharani, W., Widyantoro, D.H., Khodra M.L., 2015, “Aspect Extraction in Customer Reviews Using Syntactic Pattern”, Procedia Computer Science, Cilt 59, ss.244-253.
  • Martineau, J., Finin, T., “Delta TFIDF: An Improved Feature Space for Sentiment Analysis”, Proceedings of the Third International Conference on Weblogs and Social Media, ICWSM 2009, San Jose, Kaliforniya, ABD, ss. 258-261, 2009.
  • Mcdonald, R., Hannan, K., Neylon, T., Wells, M., Reynar, J., “Structured Models for Fine-to-Coarse Sentiment Analysis”, In Proceedings of ACL-07, 45th Annual Meeting of the Association of Computational Linguistics, Prag, Çek Cumhuriyeti, ss. 432-439, 2007.
  • Mejova, Y., Srinivasan, P., “Exploring Feature Definition and Selection for Sentiment Classifiers”, Proceedings of ICWSM ’11, 5th International AAAI Conference on Weblogs and Social Media, Barcelona, İspanya, ss. 546-549, 2011.
  • Meral, M., Diri, B., “Sentiment Analysis on Twitter”, IEEE 22nd Signal Processing and Communications Applications Conference (SIU), Trabzon, Türkiye, ss. 690-693, 2014.
  • Moghaddam, S., Ester, M., “Opinion Digger: an Unsupervised Opinion Miner from Unstructured Product Reviews”, In Proceedings of the CIKM'10, 19th ACM international conference on Information and knowledge management”, Toronto, Kanada, ss. 1825-1828, 2010.
  • Mohammad, S., Bonnie D., Cody D., “Generating High-Coverage Semantic Orientation Lexicons from Overtly Marked Words and a Thesaurus”, In Proceedings of the Conference on Empirical Methods in Natural Language Processing, Singapore, ss. 599–608, 2009.
  • MTurk, Amazon Mechanical Turk, https://www.mturk.com/, Ziyaret Tarihi: 01.10.2017.
  • Mullen, T., Collier, N., “Sentiment Analysis Using Support Vector Machines with Diverse İnformation Sources”, In Proceedings of EMNLP-2004, Conference on Empirical Methods in Natural Language Processing, Barcelona, İspanya, ss. 412-418, 2004.
  • Nakagawa, H., Mori, T., 2003, “Automatic Term Recognition based on Statistics of Compound Nouns and Their Components”, Terminology, Cilt 9, ss.201–219.
  • Nakagawa, T., Inui, K., Kurohashi, S., “Dependency Treebased Sentiment Classification using CRFs with Hidden Variables”, In Proceedings of HLT ’10, 11th Annual Conference of the North American Chapter of the Association for Computational Linguistics, Los Angeles, ABD, 786-794, 2010.
  • NLProcessor - Text Analysis Toolkit, http://www.infogistics.com/textanalysis.html, Ziyaret Tarihi: 01.10.2017.
  • OpenDover, http://opendover.nl/, Ziyaret Tarihi: 01.10.2017.
  • Pang, B., Lee, L., Vaithyanathan, S., “Thumbs up?: Sentiment Classification Using Machine Learning Techniques”, In Proceedings of EMNLP-2002, Conference on Empirical Methods in Natural Language Processing, University of Pennsylvania, PA, ABD, ss. 79-86, 2002.
  • Park, S., Lee, W., Moon, I.C., 2015, “Efficient Extraction of Domain Specific Sentiment Lexicon with Active Learning”, Pattern Recognition Letters, Cilt 56, ss.38-44.
  • Popescu, A.M., Etzioni, O., “Extracting Product Features and Opinions from Reviews”, In Proceedings of EMNLP-2005, Conference on Empirical Methods in Natural Language Processing. Vancouver, Canada, ss. 339-346, 2005.
  • Qiu, G., Liu, B., Bu, J., Chen, C., 2011, “Opinion Word Expansion and Target Extraction through Double Propagation”, Computational Linguistics, Cilt 37, ss.9-27.
  • Quan, C., Ren, F., 2014, “Unsupervised product feature extraction for feature-oriented opinion deermination”, Information Sciences, Cilt 272, ss.16-28.
  • Rabiner, L.R., 1989, “A tutorial on Hidden Markov Models and Selected Applications in Speech Recognition”, Proceedings of the IEEE, Cilt 77, ss.257-286.
  • Sevindi, B.İ., 2013, Türkçe Metinlerde Denetimli Ve Sözlük Tabanlı Duygu Analizi Yaklaşımlarının Karşılaştırılması, Yüksek Lisans Tezi, Gazi Üniversitesi, Fen Bilimleri Enstitüsü, Ankara.
  • Swapna, S., Wiebe, J., “Recognizing stances in Online Debates”, In Proceedings of ACL’99, Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, Singapur, ss. 226-234, 2009.
  • Şeker, G.A., Eryiğit, G., “Initial Explorations on using CRFs for Turkish Named Entity Recognition”, In Proceedings of COLING 2012, 24th International Conference on Computational Linguistics, IIT, Bombay, Hindistan, 2459–2474, 2012.
  • Şimşek, M.U., Ozdemir, S., “Analysis of the Relation between Turkish Twitter Messages and Stock Market Index”, In Procedings of AICT ‘12, 6th Conference on Application of Information and Communication Technologies, Tiflis, Gürcistan ,ss. 1-4, 2012.
  • Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M., 2011, “Lexicon-based Methods for Sentiment Analysis”, Computational Linguistics, Cilt 37, ss.267-307.
  • Taboada, M., Caroline A., Kimberly V., “Creating Semantic Orientation Dictionaries”,In Proceedings of 5th International Conference on Language Resources and Evaluation (LREC), Cenova, İtalya, 427–432, 2006.
  • Tetsuya, N., Jeonghee, Y., “Sentiment Analysis: Capturing Favorability Using Natural Language Processing”, In Proceedings of KCAP-03, 2nd International Conference on KnowledgeCapture, Sanibel Island, FL, ABD, ss. 70-77, 2003.
  • Tong, R.M., “An Operational System for Detecting and Tracking Opinions in On-Line Discussion”, In Proceedings of SIGIR 2001 Workshop on Operational Text Classification, New Orleans, Louisiana, ABD, 2001.
  • Turney P.D., “Thumbs up or Thumbs down?: Semantic Orientation Applied to Unsupervised Classification of Reviews”, In Proceedings of ACL’02, 40th Annual Meeting of the Association for Computational Linguistics, Pennsylvania, ABD, ss. 417-424, 2002.
  • Vasileios, H., Janyce, M.W., “Effects of Adjective Orientation and Gradability on Sentence Subjectivity”, In Proceedings of COLING-2000, 18th Interntional Conference on Computational Linguistics, Saarbrücken, Almanya, ss. 299-305, 2000.
  • Wei, W., Gulla, J.A., “Sentiment Learning on Product Reviews via Sentiment Ontology Tree”, In Proceedings of ACL'10, 48th Annual Meeting of the Association for Computational Linguistics, Uppsala, İsviçre, ss. 404-413, 2010.
  • WordNet: An Electronic Lexical Database, https://wordnet.princeton.edu/, Ziyaret Tarihi: 01.10.2017.
  • Wu, Y., Zhang, Q., Huang, X., Wu, L., “Phrase Dependency Parsing for Opinion Mining”, In Proceedings of EMNLP'09, Conference on Empirical Methods in Natural Language Processing, Singapur, ss. 1533-1541, 2009.
  • Xianghua, F., Guo, L., Yanyan, G., Zhiqiang, W., 2013, “Multi-aspect Sentiment Analysis for Chinese Online Social Reviews based on Topic Modeling and HowNet lexicon”, Knowledge-Based Systems, Cilt 37, ss.186-195.
  • Zhang, K., Xie, Y., Yang, Y., Sun, A., Liu, H., Choudhary, A., 2014, “Incorporating Conditional Random Fields and Active Learning to Improve Sentiment Identification”, Neural Networks, Cilt 58, ss.60-67.
  • Zhang, L., Hua, K., Wang, H., Qian, G., Zhang, L., 2014, “Sentiment Analysis on Reviews of Mobile Users”, Procedia Computer Science, Cilt 34, ss.458-465.
  • Zhuang, L., Jing, F., Zhu, X., “Movie review Mining and Summarization”, In Proceedings of CIKM’06, 15th ACM International Conference on Information and Knowledge Management, Indianapolis, ABD, ss. 43-50, 2006.

FİKİR MADENCİLİĞİ VE DUYGU ANALİZİ, YAKLAŞIMLAR, YÖNTEMLER ÜZERİNE BİR ARAŞTIRMA

Yıl 2018, Cilt: 6 Sayı: 4, 668 - 693, 01.12.2018

Barış Özyurt Muhammet Ali Akcayol

https://doi.org/10.15317/Scitech.2018.160

Öz

Günümüzde Web uygulamalarının yaygınlaşmasıyla birlikte bireylerin fikir, düşünce ve duygularını ifade ettikleri platformların kullanımı büyük bir hızla artmıştır. Bu platformlarda bireylerden alınmış veriler çok büyük boyutlara ulaşmaktadır. Bu verilerin manuel olarak analiz edilmesi veya sınıflandırılması mümkün olmadığından otomatik analiz edilmesi ve sınıflandırılması zorunluluk haline gelmiştir. Bu nedenle fikir madenciliği ve duygu analizine yönelik araştırmalar son yıllarda giderek artmaya başlamıştır. Bu makalede fikir madenciliği ve duygu analizi konusu detaylarıyla, uygulanan yöntemlerle birlikte anlatılmış, bu alanda yapılmış olan çalışmalar incelenmiş ve literatür taraması şeklinde sunulmuştur.

Anahtar Kelimeler

Duygu analizi Fikir madenciliği

Kaynakça

  • Akba, F., Uçan, A., Sezer, E.A., Sever, H., “Assessment of Feature Selection Metrics for Sentiment Analyses: Turkish Movie Reviews”, 8th European Conference on Data Mining, Lizbon, Portekiz, 180-184, 2014.
  • Akbaş, E., 2012, Aspect Based Opinion Mining on Turkish Tweets, Yüksek Lisans Tezi, Bilkent Üniversitesi, Mühendislik ve Fen Bilimleri Enstitüsü, Ankara.
  • Akhtar, M.S., Gupta, D., Ekbal, A., Bhattacharyya, P., 2017, “Feature Selection and Ensemble Construction: A Two-Step Method for Aspect Based Sentiment Analysis”, Knowledge-Based Systems, Cilt 125, ss.116-135.
  • Aytekin, Ç., 2013, “An Opinion Mining Task in Turkish Language A Model for Assigning Opinions in Turkish Blogs to the Polarities”, Journalism and Mass Communication, Cilt 3, ss.179-198.
  • Bagheria, A., Saraee, M., Jong, F., 2013, “Care More About Customers: Unsupervised Domain-Independent Aspect Detection for Sentiment Analysis of Customer Reviews”, Knowledge-Based Systems, Cilt 52, ss.201-203.
  • Balahur, A., Hermida, J.M., Montoyo, A., 2012, “Detecting Implicit Expressions of Emotion in Text: A Comparative Analysis”, Decision Support Systems, Cilt 53, ss.742-753.
  • Baccianella, S., Esuli, A., Sebastiani, F. “SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining”, In Proceedings of the 7th Conference on Language Resources and Evaluation (LREC'10), Valletta, MT, İtalya, 2200–2204, 2010.
  • Bespalov, D., Bai, B., Qi, Y., Shokoufandeh, A., “Sentiment Classification Based on Supervised Latent n-gram Analysis”, In Proceedings of CIKM '11, 20th ACM International Conference on Information and Knowledge Management, Glasgow, İngiltere, 375-382, 2011.
  • Bhadane, C., Dalal, H., Doshi, H., 2015, “Sentiment Analysis: Measuring Opinions”, Procedia Computer Science, Cilt 45, ss.808-814.
  • Blei, D.M., Ng, A.Y., Jordan, M.I., 2003, “Latent Dirichlet Allocation”, The Journal of Machine Learning Research, Cilt 3, ss.993-1022.
  • Brody, S., Elhadad, N., “An Unsupervised Aspect-Sentiment Model for Online Reviews”, Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, LA, ABD, 804-812, 2010.
  • Can, Ü., Alataş, B., 2017, “Duygu Analizi ve Fikir Madenciliği Algoritmalarının İncelenmesi”, International Journal of Pure and Applied Sciences, Cilt 3, ss.75-111.
  • Cesa-Bianchi, N., Gentile, C., Zaniboni, L., 2006, “Incremental Algorithms for Hierarchical Classification”, Journal of Machine Learning Research, Cilt 7, ss.31-54.
  • Chen, T., Xu, R., He, Y., Wang, X., 2017, “Improving Sentiment Analysis via Sentence Type Classification using BiLSTM-CRF and CNN”, Expert Systems with Applications, Cilt 72, ss.221-230.
  • Choi, Y., Cardie, C., “Learning with Compositional Semantics as Structural Inference for Subsentential Sentiment Analysis”, In Proceedings of EMNLP'08, Conference on Empirical Methods in Natural Language Processing, Waikiki, Hawaii, 793-801, 2008.
  • Church, K.W., Hanks, P., 1990, “Word Association Norms, Mutual İnformation and Lexicography”, Computational Linguistics, Cilt 16, ss.22-29.
  • Çetin, M., Amasyalı, M.F., “Supervised and Traditional Term Weighting Methods for Sentiment Analysis”, Signal Processing and Communications Applications Conference (SIU), Haspolat, KKTC, 1-4, 2013.
  • Dalkılıç, F.E., Gelişli, S., Diri B., “Named Entity Recognition from Turkish texts”, IEEE 18. Sinyal İşleme ve İletişim Uygulamaları Kurultayı, Diyarbakır, Türkiye, ss. 918-920, 2010.
  • Davidov, D., Tsur, O., Rappoport, A., “Enhanced Sentiment Learning Using Twitter Hashtags and Smileys”, In Proceedings of COLING'10, 23rd International Conference on Computational Linguistics, Pekin, Çin, 241-249, 2010.
  • Dehkharghani, R., Saygin, Y., Yanikoglu, B., Oflazer, K., 2016, “Sentiment Analysis in Turkish at Different Granularity Levels”, Natural Language Engineering, Cilt 23.
  • Dehkharghani, R., Saygin, Y., Yanikoglu, B., Oflazer, K., 2016, “SentiTurkNet: a Turkish Polarity Lexicon for Sentiment Analysis”, Language Resources and Evaluation, Cilt: 50, ss.667-685.
  • Ding, X., Liu, B., Yu, PS., “A Holistic Lexicon-Based Approach to Opinion Mining”, In Proceedings of WSDM-2008, Conference on Web Search and Web Data Mining, Stanford ABD, 231-240, 2008.
  • Dong, Z., Dong, Q., 2006, Hownet And the Computation of Meaning, World Scientific Publishing Co. Eirinaki, M., Pisal, S., Singh, J., 2012, “Feature-Based Opinion Mining and Ranking”, Journal of Computer and System Sciences, Cilt 78, ss.1175-1184.
  • Ekici, E., Somurca, S, İ., 2016, “Ürün Özelliklerinin Konu Modelleme Yöntemi ile Çıkarılması”, Türkiye Bilişim Vakfı Bilgisayar Bilimleri Ve Mühendisliği Dergisi, Cilt: 9, ss.51-58.
  • Eroğul, U., 2009, Sentiment Analysis In Turkish, Yüksek Lisans Tezi, Orta Doğu Teknik Üniversitesi, Fen Bilimleri Enstitüsü, Ankara.
  • Eryiğit, G., “ITU Turkish NLP Web Service.”, In proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics, Göteborg, İsviçre, 1-4, 2014.
  • Fernández-Gavilanes, M., Álvarez-López, T., Juncal-Martínez, J., Costa-Montenegro, E., González-Castaño, F.J., 2016, “Unsupervised Method for Sentiment Analysis in Online Texts”, Expert Systems with Applications, Cilt 58, ss.57-75.
  • Frank E., Hall M. A., Witten I. H., 2016. The WEKA Workbench. Online Appendix for "Data Mining: Practical Machine Learning Tools and Techniques", Morgan Kaufmann, 4. Baskı.
  • Ganter, B., Wille, R., 1999, Formal Concept Analysis, Mathematical Foundation, Springer-Verlag Berlin Heidelberg.
  • Goldensohn, S.B., Hannan, K., McDonald, R., Neylon, T., Reis, G.A., Reynar, J., “Building a Sentiment Summarizer for Local Service Reviews”, In Proceedings of WWW’08 Workshop: NLP in the Information Explosion Era, Pekin, Çin, 2008.
  • Habernal, I., Ptacek, T., Steinberger, J., 2015, “Supervised Sentiment Analysis in Czech Social Media”, Information Processing & Management, Cilt 50, ss.693-707.
  • Hailong, Z., Wenyan, G., Bo, J., “Machine Learning and Lexicon Based Methods for Sentiment Classification: A Survey”, WISA'14 Proceedings of the 2014 11th Web Information System and Application Conference, Tianjin, China, 262-265, 2014.
  • Hu, M., Liu, B., “Mining and Summarizing Customer Reviews”, KDD’04, International Conference on Knowledge Discovery and Data Mining, Seattle, ABD, 168-177, 2004.
  • IMDb, http://www.imdb.com, Ziyaret Tarihi: 01.10.2017.
  • ISEAR Databank, http://emotion-research.net/toolbox/toolboxdatabase.2006-10-13.2581092615, Ziyaret Tarihi: 01.10.2017.
  • Isidro, P.M., Rafael, V.G., Francisco, G.S., “Ontology-Guided Approach to Feature-Based Opinion Mining”, NLDB’11, Proceedings of the 16th international conference on Natural language processing and information systems, Alicante, İspanya, 193-200, 2011.
  • Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T., “Target Dependent Twitter Sentiment Classification”, In Proceedings of ACL‘11, 49th Annual Meeting of the Association for Computational Linguistics, Portland, ABD, 151-160, 2011.
  • Kang, D., Park, Y., 2014, “Review-based Measurement of Customer Satisfaction in Mobile Service: Sentiment Analysis and VIKOR Approach”, Expert Systems with Applications, Cilt 41, ss.1041-1050.
  • Kang, H., Yoo, SJ., Han, D., 2012, “Senti-lexicon and Improved Naïve Bayes Algorithms for Sentiment Analysis of Restaurant Reviews”, Expert Systems with Applications, Cilt 39, ss.6000-6010.
  • Kansal, H., Toshniwal, D., 2014, “Aspect based Summarization of Context Dependent Opinion Words”, Procedia Computer Science, Cilt 35, ss.166-175.
  • Kaya, M., Fidan, G., Toroslu, I.H., “Sentiment Analysis of Turkish Political News”, In Proceedings of WI-IAT’12 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology, Macau, Çin, 174-180, 2012.
  • Kennedy, A., Inkpen, D., 2006, “Sentiment Classification of Movie Reviews Using Contextual Valence Shifters”, Computational Intelligence, Cilt 22, ss.110-125.
  • Khan, F.H., Qamar, U., Bashir, S., 2016, “eSAP - A decision support framework for enhanced sentiment analysis and polarity classification”, Information Sciences, Cilt 367-368, ss.862-873.
  • Kontopoulos, E., Berberidis, C., Dergiades, T., Bassiliades, N., 2013, “Ontology-based sentiment Analysis of Twitter Posts”, Expert Systems with Applications, Cilt 40, ss.4065-4074.
  • Kushal, D., Steve, L., Pennock, D.M., “Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews”, In Proceedings of WWW’03, 12th International Conference on World Wide Web, Budapest Congress Centre, Macaristan, ss. 519-528, 2003.
  • Küçük, D., Yazıcı, A., “Named Entity Recognition Experiments on Turkish Texts”, In Proceedings of FQAS-2009, 8th International Conference on Flexible Query Answering Systems, Roskilde, Danimarka, ss. 524-535, 2009.
  • Lafferty, J., McCallum, A., Pereira F., “Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data”, In Proceedings of ICML '01, 18th International Conference on Machine Learning, Williams College, Williamstown, MA, ABD, ss. 282–289, 2001.
  • Li, S., Zhou, L., Li, Y., 2015, “Improving Aspect Extraction by Augmenting a Frequency-Based Method with Web-Based Similarity Measures”, Information Processing and Management, Cilt 51, ss.58-67.
  • Liao, C., Fengn, C., Yang, S., Huang, H., 2016, “Topic-related Chinese Message Sentiment Analysis”, Neurocomputing, Cilt 210, ss.237-246.
  • Liu, B., 2012, Sentiment Analysis and Opinion Mining, Morgan & Claypool Publishers.
  • Liu, Q., Gao, Z., Liu, B., Zhang, Y., 2016, “Automated Rule Selection for Opinion Target Extraction”, Knowledge-Based Systems, Cilt 104, ss.74-88.
  • Maharani, W., Widyantoro, D.H., Khodra M.L., 2015, “Aspect Extraction in Customer Reviews Using Syntactic Pattern”, Procedia Computer Science, Cilt 59, ss.244-253.
  • Martineau, J., Finin, T., “Delta TFIDF: An Improved Feature Space for Sentiment Analysis”, Proceedings of the Third International Conference on Weblogs and Social Media, ICWSM 2009, San Jose, Kaliforniya, ABD, ss. 258-261, 2009.
  • Mcdonald, R., Hannan, K., Neylon, T., Wells, M., Reynar, J., “Structured Models for Fine-to-Coarse Sentiment Analysis”, In Proceedings of ACL-07, 45th Annual Meeting of the Association of Computational Linguistics, Prag, Çek Cumhuriyeti, ss. 432-439, 2007.
  • Mejova, Y., Srinivasan, P., “Exploring Feature Definition and Selection for Sentiment Classifiers”, Proceedings of ICWSM ’11, 5th International AAAI Conference on Weblogs and Social Media, Barcelona, İspanya, ss. 546-549, 2011.
  • Meral, M., Diri, B., “Sentiment Analysis on Twitter”, IEEE 22nd Signal Processing and Communications Applications Conference (SIU), Trabzon, Türkiye, ss. 690-693, 2014.
  • Moghaddam, S., Ester, M., “Opinion Digger: an Unsupervised Opinion Miner from Unstructured Product Reviews”, In Proceedings of the CIKM'10, 19th ACM international conference on Information and knowledge management”, Toronto, Kanada, ss. 1825-1828, 2010.
  • Mohammad, S., Bonnie D., Cody D., “Generating High-Coverage Semantic Orientation Lexicons from Overtly Marked Words and a Thesaurus”, In Proceedings of the Conference on Empirical Methods in Natural Language Processing, Singapore, ss. 599–608, 2009.
  • MTurk, Amazon Mechanical Turk, https://www.mturk.com/, Ziyaret Tarihi: 01.10.2017.
  • Mullen, T., Collier, N., “Sentiment Analysis Using Support Vector Machines with Diverse İnformation Sources”, In Proceedings of EMNLP-2004, Conference on Empirical Methods in Natural Language Processing, Barcelona, İspanya, ss. 412-418, 2004.
  • Nakagawa, H., Mori, T., 2003, “Automatic Term Recognition based on Statistics of Compound Nouns and Their Components”, Terminology, Cilt 9, ss.201–219.
  • Nakagawa, T., Inui, K., Kurohashi, S., “Dependency Treebased Sentiment Classification using CRFs with Hidden Variables”, In Proceedings of HLT ’10, 11th Annual Conference of the North American Chapter of the Association for Computational Linguistics, Los Angeles, ABD, 786-794, 2010.
  • NLProcessor - Text Analysis Toolkit, http://www.infogistics.com/textanalysis.html, Ziyaret Tarihi: 01.10.2017.
  • OpenDover, http://opendover.nl/, Ziyaret Tarihi: 01.10.2017.
  • Pang, B., Lee, L., Vaithyanathan, S., “Thumbs up?: Sentiment Classification Using Machine Learning Techniques”, In Proceedings of EMNLP-2002, Conference on Empirical Methods in Natural Language Processing, University of Pennsylvania, PA, ABD, ss. 79-86, 2002.
  • Park, S., Lee, W., Moon, I.C., 2015, “Efficient Extraction of Domain Specific Sentiment Lexicon with Active Learning”, Pattern Recognition Letters, Cilt 56, ss.38-44.
  • Popescu, A.M., Etzioni, O., “Extracting Product Features and Opinions from Reviews”, In Proceedings of EMNLP-2005, Conference on Empirical Methods in Natural Language Processing. Vancouver, Canada, ss. 339-346, 2005.
  • Qiu, G., Liu, B., Bu, J., Chen, C., 2011, “Opinion Word Expansion and Target Extraction through Double Propagation”, Computational Linguistics, Cilt 37, ss.9-27.
  • Quan, C., Ren, F., 2014, “Unsupervised product feature extraction for feature-oriented opinion deermination”, Information Sciences, Cilt 272, ss.16-28.
  • Rabiner, L.R., 1989, “A tutorial on Hidden Markov Models and Selected Applications in Speech Recognition”, Proceedings of the IEEE, Cilt 77, ss.257-286.
  • Sevindi, B.İ., 2013, Türkçe Metinlerde Denetimli Ve Sözlük Tabanlı Duygu Analizi Yaklaşımlarının Karşılaştırılması, Yüksek Lisans Tezi, Gazi Üniversitesi, Fen Bilimleri Enstitüsü, Ankara.
  • Swapna, S., Wiebe, J., “Recognizing stances in Online Debates”, In Proceedings of ACL’99, Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, Singapur, ss. 226-234, 2009.
  • Şeker, G.A., Eryiğit, G., “Initial Explorations on using CRFs for Turkish Named Entity Recognition”, In Proceedings of COLING 2012, 24th International Conference on Computational Linguistics, IIT, Bombay, Hindistan, 2459–2474, 2012.
  • Şimşek, M.U., Ozdemir, S., “Analysis of the Relation between Turkish Twitter Messages and Stock Market Index”, In Procedings of AICT ‘12, 6th Conference on Application of Information and Communication Technologies, Tiflis, Gürcistan ,ss. 1-4, 2012.
  • Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M., 2011, “Lexicon-based Methods for Sentiment Analysis”, Computational Linguistics, Cilt 37, ss.267-307.
  • Taboada, M., Caroline A., Kimberly V., “Creating Semantic Orientation Dictionaries”,In Proceedings of 5th International Conference on Language Resources and Evaluation (LREC), Cenova, İtalya, 427–432, 2006.
  • Tetsuya, N., Jeonghee, Y., “Sentiment Analysis: Capturing Favorability Using Natural Language Processing”, In Proceedings of KCAP-03, 2nd International Conference on KnowledgeCapture, Sanibel Island, FL, ABD, ss. 70-77, 2003.
  • Tong, R.M., “An Operational System for Detecting and Tracking Opinions in On-Line Discussion”, In Proceedings of SIGIR 2001 Workshop on Operational Text Classification, New Orleans, Louisiana, ABD, 2001.
  • Turney P.D., “Thumbs up or Thumbs down?: Semantic Orientation Applied to Unsupervised Classification of Reviews”, In Proceedings of ACL’02, 40th Annual Meeting of the Association for Computational Linguistics, Pennsylvania, ABD, ss. 417-424, 2002.
  • Vasileios, H., Janyce, M.W., “Effects of Adjective Orientation and Gradability on Sentence Subjectivity”, In Proceedings of COLING-2000, 18th Interntional Conference on Computational Linguistics, Saarbrücken, Almanya, ss. 299-305, 2000.
  • Wei, W., Gulla, J.A., “Sentiment Learning on Product Reviews via Sentiment Ontology Tree”, In Proceedings of ACL'10, 48th Annual Meeting of the Association for Computational Linguistics, Uppsala, İsviçre, ss. 404-413, 2010.
  • WordNet: An Electronic Lexical Database, https://wordnet.princeton.edu/, Ziyaret Tarihi: 01.10.2017.
  • Wu, Y., Zhang, Q., Huang, X., Wu, L., “Phrase Dependency Parsing for Opinion Mining”, In Proceedings of EMNLP'09, Conference on Empirical Methods in Natural Language Processing, Singapur, ss. 1533-1541, 2009.
  • Xianghua, F., Guo, L., Yanyan, G., Zhiqiang, W., 2013, “Multi-aspect Sentiment Analysis for Chinese Online Social Reviews based on Topic Modeling and HowNet lexicon”, Knowledge-Based Systems, Cilt 37, ss.186-195.
  • Zhang, K., Xie, Y., Yang, Y., Sun, A., Liu, H., Choudhary, A., 2014, “Incorporating Conditional Random Fields and Active Learning to Improve Sentiment Identification”, Neural Networks, Cilt 58, ss.60-67.
  • Zhang, L., Hua, K., Wang, H., Qian, G., Zhang, L., 2014, “Sentiment Analysis on Reviews of Mobile Users”, Procedia Computer Science, Cilt 34, ss.458-465.
  • Zhuang, L., Jing, F., Zhu, X., “Movie review Mining and Summarization”, In Proceedings of CIKM’06, 15th ACM International Conference on Information and Knowledge Management, Indianapolis, ABD, ss. 43-50, 2006.
Toplam 87 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Barış Özyurt Bu kişi benim

Muhammet Ali Akcayol

Yayımlanma Tarihi 1 Aralık 2018
Yayımlandığı Sayı Yıl 2018 Cilt: 6 Sayı: 4

Kaynak Göster

APA Özyurt, B., & Akcayol, M. A. (2018). FİKİR MADENCİLİĞİ VE DUYGU ANALİZİ, YAKLAŞIMLAR, YÖNTEMLER ÜZERİNE BİR ARAŞTIRMA. Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi, 6(4), 668-693. https://doi.org/10.15317/Scitech.2018.160
AMA Özyurt B, Akcayol MA. FİKİR MADENCİLİĞİ VE DUYGU ANALİZİ, YAKLAŞIMLAR, YÖNTEMLER ÜZERİNE BİR ARAŞTIRMA. sujest. Aralık 2018;6(4):668-693. doi:10.15317/Scitech.2018.160
Chicago Özyurt, Barış, ve Muhammet Ali Akcayol. “FİKİR MADENCİLİĞİ VE DUYGU ANALİZİ, YAKLAŞIMLAR, YÖNTEMLER ÜZERİNE BİR ARAŞTIRMA”. Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi 6, sy. 4 (Aralık 2018): 668-93. https://doi.org/10.15317/Scitech.2018.160.
EndNote Özyurt B, Akcayol MA (01 Aralık 2018) FİKİR MADENCİLİĞİ VE DUYGU ANALİZİ, YAKLAŞIMLAR, YÖNTEMLER ÜZERİNE BİR ARAŞTIRMA. Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi 6 4 668–693.
IEEE B. Özyurt ve M. A. Akcayol, “FİKİR MADENCİLİĞİ VE DUYGU ANALİZİ, YAKLAŞIMLAR, YÖNTEMLER ÜZERİNE BİR ARAŞTIRMA”, sujest, c. 6, sy. 4, ss. 668–693, 2018, doi: 10.15317/Scitech.2018.160.
ISNAD Özyurt, Barış - Akcayol, Muhammet Ali. “FİKİR MADENCİLİĞİ VE DUYGU ANALİZİ, YAKLAŞIMLAR, YÖNTEMLER ÜZERİNE BİR ARAŞTIRMA”. Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi 6/4 (Aralık 2018), 668-693. https://doi.org/10.15317/Scitech.2018.160.
JAMA Özyurt B, Akcayol MA. FİKİR MADENCİLİĞİ VE DUYGU ANALİZİ, YAKLAŞIMLAR, YÖNTEMLER ÜZERİNE BİR ARAŞTIRMA. sujest. 2018;6:668–693.
MLA Özyurt, Barış ve Muhammet Ali Akcayol. “FİKİR MADENCİLİĞİ VE DUYGU ANALİZİ, YAKLAŞIMLAR, YÖNTEMLER ÜZERİNE BİR ARAŞTIRMA”. Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi, c. 6, sy. 4, 2018, ss. 668-93, doi:10.15317/Scitech.2018.160.
Vancouver Özyurt B, Akcayol MA. FİKİR MADENCİLİĞİ VE DUYGU ANALİZİ, YAKLAŞIMLAR, YÖNTEMLER ÜZERİNE BİR ARAŞTIRMA. sujest. 2018;6(4):668-93.

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