Review
BibTex RIS Cite

DUYGU ANALİZİ VE FİKİR MADENCİLİĞİ UYGULAMALARI ÜZERİNE LİTERATÜR TARAMASI

Year 2021, Volume: 24 Issue: 2, 93 - 114, 02.06.2021

Hatice Elif Ekim , A. Burak İnner

https://doi.org/10.17780/ksujes.819367

Abstract

Duygu analizi ve fikir madenciliği, kişilerin, bir konu, grup, ürün, marka veya durum ile ilgili görüşlerini belirttiği metinleri, doğal dil işleme, yapay zeka veya istatistik alanlarından uygulamalar yardımıyla analiz ederek anlamlandırma çalışmalarıdır. Son yıllarda, sosyal medya ve kullanıcıların fikir paylaştığı diğer platformların kullanımının artmasıyla saatte terabaytlar seviyesine ulaşan veri miktarı, duygu analizi ve fikir madenciliği konularına verilen önemi artırmıştır.
Bu çalışma kapsamında, duygu analizinde makine öğrenimi yaklaşımları, sözlük tabanlı yaklaşımlar ve hibrit yaklaşım üzerine güncel makaleler incelenerek, makaleler ile ilgili literatür çalışması araştırmacılara sunulmuştur. İncelenen makalelerden, makalenin yayınlanma tarihi, araştırma problemi, yaklaşım, önişleme ve öznitelik seçme metotları, sınıflandırma algoritması, model başarı ölçütü, başarı oranı en yüksek algoritma ve başarı oranı, veri kaynağı bilgilerinin yer aldığı bir tablo oluşturulmuştur. Makine öğrenimi tabanlı yöntemlerin sıklıkla tercih edilmesi ve çalışma sayısının diğer yöntemlerden fazla olması sebebiyle, denetimli, denetimsiz, yarı denetimli ve derin öğrenme başlıkları altında ayrı ayrı ele alınmıştır. Çalışma sonucunda, incelenen makaleler ışığında genel bir değerlendirme ile sonuç çıkarılarak çalışma tamamlanmıştır.

Keywords

Duygu analizi Fikir madenciliği Makine öğrenmesi Derin öğrenme

References

  • Çetin, F. S. ve Eryiğit, G. (2018). Türkçe Hedef Tabanlı Duygu Analizi İçin Alt Görevlerin İncelenmesi – Hedef Terim, Hedef Kategori Ve Duygu Sınıfı Belirleme. Bilişim Teknolojileri Dergisi, 11, 43–56.
  • Kaynar, O., Yıldız, M., Görmez, Y. ve Albayrak, A. (2016). Makine Öğrenmesi Yöntemleri ile Duygu Analizi. In International Artificial Intelligence and Data Processing Symposium (IDAP'16), 234–241.
  • Nasukawa, T., Yi J. (2003). Sentiment analysis: Capturing favorability using natural language processing, K-CAP 2003, 70-77.
  • Liu, B. (2012). Sentiment Analysis and Opinion Mining. Morgan& Claypool Publishers, 7.
  • Esuli, A. ve Sebastiani, F. (2006). Sentiwordnet: A publicly available lexical resource for opinion mining. Proceedings of the 5th International Conference on Language Resources and Evaluation, LREC 2006, 417–422.
  • Baccianella, S., Esuli, A. ve Sebastiani, F. (2010). Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. Proceedings of the 7th International Conference on Language Resources and Evaluation, LREC 2010, 2200–2204.
  • Neviarouskaya, A., Prendinger, H. ve Ishizuka, M. (2011). Affect Analysis Model: Novel rule-based approach to affect sensing from text. Natural Language Engineering, 17, 95–135.
  • Medhat, W., Hassan, A. ve Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5, 1093–1113.
  • Akçayol, M., ve Özyurt, B. (2018). Fikir Madenciliği ve Duygu Analizi, Yaklaşımlar, Yöntemler Üzerine Bir Araştırma. Selcuk University Journal of Engineering. Science and Technology, 6, 668-693.
  • Medhat, W., Hassan, A. ve Mohamed, H. K. (2014). Combined algorithm for data mining using association rules. Ain Shams J. Electr. Eng., 1, 1–12.
  • Maynard, D. ve Funk A. (2011). Automatic detection of political opinions in tweets. CEUR Workshop Proceedings, 718, 81–92.
  • Şeker, A., Diri, B. ve Balık, H. H. (2017). Derin Öğrenme Yöntemleri ve Uygulamaları Hakkında Bir İnceleme. Gazi Mühendislik Bilimleri Dergisi, 3, 47–64.
  • Pang, B., Lee, L. ve Vaithyanathan, S. (2002). Thumbs up? Sentiment Classification using Machine Learning Techniques.
  • Go, A., Bhayani, R. ve Huang, L. (2009). Twitter Sentiment Classification using Distant Supervision. CS224N project report, Stanford.
  • Turney, P. D. (2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. ACL ’02 Proc. 40th Annu. Meet. Assoc. Comput. Linguistics, 417–424.
  • Asghar, M. Z., Ullah, R., Ahmad, S., Kundi, F. M. ve Nawaz, I. U. (2014). Lexicon based approach for sentiment classification of user reviews. Life Science Journal, 11, 468–473.
  • Taboada, M., Brooke, J., Tofiloski, M., Voll, K. ve Stede, M. (2011). Lexicon-Based methods for sentiment analysis. Computational Linguistics, 37, 267-307.
  • Hu, M. ve Liu, B. (2004). Mining and Summarizing Customer Reviews. Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining ,168–177.
  • Dehkharghani, R., Saygin, Y., Yanikoglu, B. ve Oflazer, K. (2016). SentiTurkNet: a Turkish polarity lexicon for sentiment analysis. Language Resources and Evaluation, 50, 667–685.
  • Özsert, C. M. ve Özgür, A. (2013). Word Polarity Detection Using a Multilingual Approach. In International Conference on Intelligent Text Processing and Computational Linguistics, 75–82.
  • Mukwazvure, A. ve Supreethi, K. P. (2015). A hybrid approach to sentiment analysis of news comments. 2015 4th Int. Conf. Reliab. Infocom Technol. Optim. Trends Futur. Dir. ICRITO, 1–6.
  • Agarwal, A., Xie, B., Vovsha, I., Rambow, O. ve Passonneau, R. (2011). Sentiment analysis of twitter data. In Proceedings of the workshop on language in social media, 30–38.
  • Çoban, O., Özyer, B. ve Özyer, G. T. (2015). Türkçe Twitter Mesajlarının Duygu Analizi. In 2015 23nd Signal Processing and Communications Applications Conference (SIU), 2388–2391.
  • Turkmen, A. C. ve Cemgil, A. T. (2014). Mikroblog verilerinden politik ilgililik ve eǧilim tahmini. In 2014 22nd Signal Process. Commun. Appl. Conf. SIU 2014 – Proc, 1327–1330.
  • Chen, C. C. ve Tseng, Y. D. (2011). Quality evaluation of product reviews using an information quality framework. Decision Support Systems, 50, 755–768.
  • Mikolov, T., Chen, K., Corrado, G. ve Dean, J. (2013). Efficient estimation of word representations in vector space. 1st International Conference on Learning Representations, ICLR 2013 - Workshop Track Proceedings, 1–12.
  • Mikolov, T., Sutskever, I., Chen, K., Corrado, G. ve Dean, J. (2013). Distributed Representations of Words and Phrases and Their Compositionality. Advances in Neural Information Processing Systems, 1–9.
  • Bojanowski, P., Grave, E., Joulin, A. ve Mikolov, T. (2017). Enriching Word Vectors with Subword Information. Transactions of the Association for Computational Linguistics, 5, 135–146.
  • Pennington, J., Socher, R ve Manning, C. D. (2014). GloVe: Global Vectors for Word Representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1532– 1543.
  • Osmanoğlu, U. Ö., Atak, O. N., Çağlar, K., Kayhan, H. ve Can, T. (2020). Sentiment Analysis for Distance Education Course Materials: A Machine Learning Approach. Journal of Educational Technology and Online Learning, 3, 31–48.
  • Ayvaz, S., Yıldırım, S. ve Salman, Y. B. (2019). Türkçe Duygu Kütüphanesi Geliştirme: Sosyal Medya Verileriyle Duygu Analizi Çalışması. European Journal of Science and Technology, 16, 51–60.
  • Erşahin, B., Aktaş, Ö., Kılınç, D. ve Erşahin, M. (2019). A hybrid sentiment analysis method for Turkish. Turkish Journal of Electrical Engineering and Computer Science, 27, 1780–1793.
  • Yurtalan, G., Koyuncu, M. ve Turhan, Ç. (2019). A polarity calculation approach for lexicon-based Turkish sentiment analysis. Turkish Journal of Electrical Engineering and Computer Science, 27, 1325–1339.
  • El Rahman, S. A., Alotaibi, F. A. ve Alshehri, W. A. (2019). Sentiment Analysis of Twitter Data. In 2019 International Conference on Computer and Information Sciences (ICCIS), 1–4.
  • Bilgin, M. ve Şentürk, İ. F. (2019). Danışmanlı ve yarı danışmanlı öğrenme kullanarak doküman vektörleri tabanlı tweetlerin duygu analizi. Balıkesir Üniversitesi Fen Bilim. Enstitüsü Dergisi, 21, 822–839.
  • Çelik, Ö. ve Aslan, A. F. (2019). Gender Prediction from Social Media Comments with Artificial Intelligence. Sakarya University Journal of Science, 23, 1256–1264.
  • Al-Hadhrami, S., Al-Fassam, N. ve Benhidour, H. (2019). Sentiment Analysis of English Tweets: A Comparative Study of Supervised and Unsupervised Approaches. 2nd International Conference on Computer Applications and Information Security, ICCAIS 2019, 1–5.
  • Rumelli, M., Akkuş, D., Kart, Ö. ve Işık, Z. (2019). Sentiment Analysis in Turkish Text with Machine Learning Algorithms. In 2019 Innovations in Intelligent Systems and Applications Conference (ASYU), 1–5.
  • Kamiş, S. ve Goularas, D. (2019). Evaluation of Deep Learning Techniques in Sentiment Analysis from Twitter Data. In 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML), 12–17.
  • Ray, P. ve Chakrabarti, A. (2019). A Mixed approach of Deep Learning method and Rule-Based method to improve Aspect Level Sentiment Analysis. Applied Computing and Informatics.
  • Shan Lee, V. L., Gan, K. H., Tan, T. P. ve Abdullah, R. (2019). Semi-supervised learning for sentiment classification using small number of labeled data. Procedia Computer Science, 161, 577–584.
  • John, A., John, A. ve Sheik, R. (2019). Context Deployed Sentiment Analysis Using Hybrid Lexicon. In 2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT), 1–5.
  • Çoban, Ö. ve Özyer, G. T. (2018). Word2vec and Clustering based Twitter Sentiment Analysis. In 2018 International Conference on Artificial Intelligence and Data Processing (IDAP).
  • Çiftçi, B. ve Apaydın, M. S. (2018). A Deep Learning Approach to Sentiment Analysis in Turkish. In 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), 1–5.
  • Yüksel, A. S. ve Tan, F. G. (2018). Metin Madenciliği Teknikleri İle Sosyal Ağlarda Bilgi Keşfi. Mühendislik Bilimleri ve Tasarım Dergisi, 6, 324–333.
  • Naz, S., Sharan, A. ve Malik, N. (2018). Sentiment Classification on Twitter Data Using Support Vector Machine. In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), 676–679.
  • Desai, R. D. (2018). Sentiment Analysis of Twitter Data. In 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), 114–117.
  • Kurniawati, I. ve Pardede, H. F. (2018). Hybrid Method of Information Gain and Particle Swarm Optimization for Selection of Features of SVM-Based Sentiment Analysis. In 2018 International Conference on Information Technology Systems and Innovation (ICITSI), 1–5.
  • Salur, M. U. ve Aydın, I. (2018). Sentiment classification based on deep learning. In 2018 26th Signal Processing and Communications Applications Conference (SIU), 1–4.
  • Rane, A. ve Kumar, A. (2018). Sentiment Classification System of Twitter Data for US Airline Service Analysis. In 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), 1, 769–773.
  • Parlar, T., Saraç, E. ve Özel, S. A. (2017). Comparison of Feature Selection Methods for Sentiment Analysis on Turkish Twitter data. In 2017 25th Signal Processing and Communications Applications Conference (SIU), 1–4.
  • Pervan, N. ve Keleş, H. Y. (2017). Sentiment Analysis Using A Random Forest Classifier On Turkish Web Comments. Communications Faculty Of Science University of Ankara, 59, 69–79.
  • Hayran, A. ve Sert, M. (2017). Sentiment Analysis on Microblog Data Based on Word Embedding and Fusion Techniques. In 2017 25th Signal Processing and Communications Applications Conference (SIU), 1–4.
  • Onan, A. (2017). “Twitter Mesajları Üzerinde Makine Öğrenmesi Yöntemlerine Dayalı Duygu Analizi. Yönetim Bilişim Sistemleri Dergisi, 3, 1–14.
  • Ding, Y., Li, B., Zhao, Y. ve Cheng, C. (2017). Scoring tourist attractions based on sentiment lexicon. In 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 1990–1993.
  • Kaynar, O., Aydın, Z. ve Görmez, Y. (2017). Sentiment Analizinde Öznitelik Düşürme Yöntemlerinin Oto Kodlayıcılı Derin Öğrenme Makinaları ile Karşılaştırılması. Bilişim Teknolojileri Dergisi, 10, 319–326.
  • Hassan, A. ve Mahmood, A. (2017). Deep Learning approach for sentiment analysis of short texts. In 2017 3rd international conference on control, automation and robotics (ICCAR), 705–710.
  • Cliche, M. (2017). BB twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs.
  • Atalay, M. ve Çelik, E. (2017). Büyük Veri Anali̇zi̇nde Yapay Zekâ Ve Maki̇ne Öğrenmesi̇ Uygulamaları - Artificial Intelligence and Machine Learning Applications in Big Data Analysis. Mehmet Akif Ersoy Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 9, 155–172.
  • Witten, I.H. ve Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques.
  • Chakraborty, K., Bag, R. ve Bhattacharyya, S. (2018). Relook into Sentiment Analysis performed on Indian Languages using Deep Learning. In 2018 Fourth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), 208–213.
  • Chen, Y. ve Zhang, Z. (2018). Research on text sentiment analysis based on CNNs and SVM. Proc. 13th IEEE Conf. Ind. Electron. Appl. ICIEA 2018, 2731–2734.

LITERATURE REVIEW ON SENTIMENT ANALYSIS AND OPINION MINING APPLICATIONS

Year 2021, Volume: 24 Issue: 2, 93 - 114, 02.06.2021

Hatice Elif Ekim , A. Burak İnner

https://doi.org/10.17780/ksujes.819367

Abstract

Sentiment analysis and opinion mining are the studies of interpretation by analyzing texts in which people express their opinions about a subject, group, product, brand, or situation with applications with natural language processing, artificial intelligence, or statistics. In recent years, with the increase in the use of social media and other platforms where users share ideas, the amount of data reaching the level of terabytes per hour has increased the importance given to sentiment analysis and opinion mining.
Within the scope of this study, a literature review on current articles and articles on machine learning approaches, lexicon-based approaches, and hybrid approach in sentiment analysis is presented to the researchers. From the articles examined, a table containing the publication date of the article, research problem, approach, preprocessing and feature selection methods, classification algorithm, model success criterion, an algorithm with the highest success rate and success rate, data source information was created. Since machine learning-based methods are frequently preferred and the number of studies is higher than other methods, they are discussed separately under the titles of supervised, unsupervised, semi-supervised, and deep learning. At the end of the study, the study was completed by making a general evaluation in light of the articles examined.

Keywords

Sentiment analysis Opinion mining Machine learning Deep learning

References

  • Çetin, F. S. ve Eryiğit, G. (2018). Türkçe Hedef Tabanlı Duygu Analizi İçin Alt Görevlerin İncelenmesi – Hedef Terim, Hedef Kategori Ve Duygu Sınıfı Belirleme. Bilişim Teknolojileri Dergisi, 11, 43–56.
  • Kaynar, O., Yıldız, M., Görmez, Y. ve Albayrak, A. (2016). Makine Öğrenmesi Yöntemleri ile Duygu Analizi. In International Artificial Intelligence and Data Processing Symposium (IDAP'16), 234–241.
  • Nasukawa, T., Yi J. (2003). Sentiment analysis: Capturing favorability using natural language processing, K-CAP 2003, 70-77.
  • Liu, B. (2012). Sentiment Analysis and Opinion Mining. Morgan& Claypool Publishers, 7.
  • Esuli, A. ve Sebastiani, F. (2006). Sentiwordnet: A publicly available lexical resource for opinion mining. Proceedings of the 5th International Conference on Language Resources and Evaluation, LREC 2006, 417–422.
  • Baccianella, S., Esuli, A. ve Sebastiani, F. (2010). Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. Proceedings of the 7th International Conference on Language Resources and Evaluation, LREC 2010, 2200–2204.
  • Neviarouskaya, A., Prendinger, H. ve Ishizuka, M. (2011). Affect Analysis Model: Novel rule-based approach to affect sensing from text. Natural Language Engineering, 17, 95–135.
  • Medhat, W., Hassan, A. ve Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5, 1093–1113.
  • Akçayol, M., ve Özyurt, B. (2018). Fikir Madenciliği ve Duygu Analizi, Yaklaşımlar, Yöntemler Üzerine Bir Araştırma. Selcuk University Journal of Engineering. Science and Technology, 6, 668-693.
  • Medhat, W., Hassan, A. ve Mohamed, H. K. (2014). Combined algorithm for data mining using association rules. Ain Shams J. Electr. Eng., 1, 1–12.
  • Maynard, D. ve Funk A. (2011). Automatic detection of political opinions in tweets. CEUR Workshop Proceedings, 718, 81–92.
  • Şeker, A., Diri, B. ve Balık, H. H. (2017). Derin Öğrenme Yöntemleri ve Uygulamaları Hakkında Bir İnceleme. Gazi Mühendislik Bilimleri Dergisi, 3, 47–64.
  • Pang, B., Lee, L. ve Vaithyanathan, S. (2002). Thumbs up? Sentiment Classification using Machine Learning Techniques.
  • Go, A., Bhayani, R. ve Huang, L. (2009). Twitter Sentiment Classification using Distant Supervision. CS224N project report, Stanford.
  • Turney, P. D. (2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. ACL ’02 Proc. 40th Annu. Meet. Assoc. Comput. Linguistics, 417–424.
  • Asghar, M. Z., Ullah, R., Ahmad, S., Kundi, F. M. ve Nawaz, I. U. (2014). Lexicon based approach for sentiment classification of user reviews. Life Science Journal, 11, 468–473.
  • Taboada, M., Brooke, J., Tofiloski, M., Voll, K. ve Stede, M. (2011). Lexicon-Based methods for sentiment analysis. Computational Linguistics, 37, 267-307.
  • Hu, M. ve Liu, B. (2004). Mining and Summarizing Customer Reviews. Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining ,168–177.
  • Dehkharghani, R., Saygin, Y., Yanikoglu, B. ve Oflazer, K. (2016). SentiTurkNet: a Turkish polarity lexicon for sentiment analysis. Language Resources and Evaluation, 50, 667–685.
  • Özsert, C. M. ve Özgür, A. (2013). Word Polarity Detection Using a Multilingual Approach. In International Conference on Intelligent Text Processing and Computational Linguistics, 75–82.
  • Mukwazvure, A. ve Supreethi, K. P. (2015). A hybrid approach to sentiment analysis of news comments. 2015 4th Int. Conf. Reliab. Infocom Technol. Optim. Trends Futur. Dir. ICRITO, 1–6.
  • Agarwal, A., Xie, B., Vovsha, I., Rambow, O. ve Passonneau, R. (2011). Sentiment analysis of twitter data. In Proceedings of the workshop on language in social media, 30–38.
  • Çoban, O., Özyer, B. ve Özyer, G. T. (2015). Türkçe Twitter Mesajlarının Duygu Analizi. In 2015 23nd Signal Processing and Communications Applications Conference (SIU), 2388–2391.
  • Turkmen, A. C. ve Cemgil, A. T. (2014). Mikroblog verilerinden politik ilgililik ve eǧilim tahmini. In 2014 22nd Signal Process. Commun. Appl. Conf. SIU 2014 – Proc, 1327–1330.
  • Chen, C. C. ve Tseng, Y. D. (2011). Quality evaluation of product reviews using an information quality framework. Decision Support Systems, 50, 755–768.
  • Mikolov, T., Chen, K., Corrado, G. ve Dean, J. (2013). Efficient estimation of word representations in vector space. 1st International Conference on Learning Representations, ICLR 2013 - Workshop Track Proceedings, 1–12.
  • Mikolov, T., Sutskever, I., Chen, K., Corrado, G. ve Dean, J. (2013). Distributed Representations of Words and Phrases and Their Compositionality. Advances in Neural Information Processing Systems, 1–9.
  • Bojanowski, P., Grave, E., Joulin, A. ve Mikolov, T. (2017). Enriching Word Vectors with Subword Information. Transactions of the Association for Computational Linguistics, 5, 135–146.
  • Pennington, J., Socher, R ve Manning, C. D. (2014). GloVe: Global Vectors for Word Representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1532– 1543.
  • Osmanoğlu, U. Ö., Atak, O. N., Çağlar, K., Kayhan, H. ve Can, T. (2020). Sentiment Analysis for Distance Education Course Materials: A Machine Learning Approach. Journal of Educational Technology and Online Learning, 3, 31–48.
  • Ayvaz, S., Yıldırım, S. ve Salman, Y. B. (2019). Türkçe Duygu Kütüphanesi Geliştirme: Sosyal Medya Verileriyle Duygu Analizi Çalışması. European Journal of Science and Technology, 16, 51–60.
  • Erşahin, B., Aktaş, Ö., Kılınç, D. ve Erşahin, M. (2019). A hybrid sentiment analysis method for Turkish. Turkish Journal of Electrical Engineering and Computer Science, 27, 1780–1793.
  • Yurtalan, G., Koyuncu, M. ve Turhan, Ç. (2019). A polarity calculation approach for lexicon-based Turkish sentiment analysis. Turkish Journal of Electrical Engineering and Computer Science, 27, 1325–1339.
  • El Rahman, S. A., Alotaibi, F. A. ve Alshehri, W. A. (2019). Sentiment Analysis of Twitter Data. In 2019 International Conference on Computer and Information Sciences (ICCIS), 1–4.
  • Bilgin, M. ve Şentürk, İ. F. (2019). Danışmanlı ve yarı danışmanlı öğrenme kullanarak doküman vektörleri tabanlı tweetlerin duygu analizi. Balıkesir Üniversitesi Fen Bilim. Enstitüsü Dergisi, 21, 822–839.
  • Çelik, Ö. ve Aslan, A. F. (2019). Gender Prediction from Social Media Comments with Artificial Intelligence. Sakarya University Journal of Science, 23, 1256–1264.
  • Al-Hadhrami, S., Al-Fassam, N. ve Benhidour, H. (2019). Sentiment Analysis of English Tweets: A Comparative Study of Supervised and Unsupervised Approaches. 2nd International Conference on Computer Applications and Information Security, ICCAIS 2019, 1–5.
  • Rumelli, M., Akkuş, D., Kart, Ö. ve Işık, Z. (2019). Sentiment Analysis in Turkish Text with Machine Learning Algorithms. In 2019 Innovations in Intelligent Systems and Applications Conference (ASYU), 1–5.
  • Kamiş, S. ve Goularas, D. (2019). Evaluation of Deep Learning Techniques in Sentiment Analysis from Twitter Data. In 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML), 12–17.
  • Ray, P. ve Chakrabarti, A. (2019). A Mixed approach of Deep Learning method and Rule-Based method to improve Aspect Level Sentiment Analysis. Applied Computing and Informatics.
  • Shan Lee, V. L., Gan, K. H., Tan, T. P. ve Abdullah, R. (2019). Semi-supervised learning for sentiment classification using small number of labeled data. Procedia Computer Science, 161, 577–584.
  • John, A., John, A. ve Sheik, R. (2019). Context Deployed Sentiment Analysis Using Hybrid Lexicon. In 2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT), 1–5.
  • Çoban, Ö. ve Özyer, G. T. (2018). Word2vec and Clustering based Twitter Sentiment Analysis. In 2018 International Conference on Artificial Intelligence and Data Processing (IDAP).
  • Çiftçi, B. ve Apaydın, M. S. (2018). A Deep Learning Approach to Sentiment Analysis in Turkish. In 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), 1–5.
  • Yüksel, A. S. ve Tan, F. G. (2018). Metin Madenciliği Teknikleri İle Sosyal Ağlarda Bilgi Keşfi. Mühendislik Bilimleri ve Tasarım Dergisi, 6, 324–333.
  • Naz, S., Sharan, A. ve Malik, N. (2018). Sentiment Classification on Twitter Data Using Support Vector Machine. In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), 676–679.
  • Desai, R. D. (2018). Sentiment Analysis of Twitter Data. In 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), 114–117.
  • Kurniawati, I. ve Pardede, H. F. (2018). Hybrid Method of Information Gain and Particle Swarm Optimization for Selection of Features of SVM-Based Sentiment Analysis. In 2018 International Conference on Information Technology Systems and Innovation (ICITSI), 1–5.
  • Salur, M. U. ve Aydın, I. (2018). Sentiment classification based on deep learning. In 2018 26th Signal Processing and Communications Applications Conference (SIU), 1–4.
  • Rane, A. ve Kumar, A. (2018). Sentiment Classification System of Twitter Data for US Airline Service Analysis. In 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), 1, 769–773.
  • Parlar, T., Saraç, E. ve Özel, S. A. (2017). Comparison of Feature Selection Methods for Sentiment Analysis on Turkish Twitter data. In 2017 25th Signal Processing and Communications Applications Conference (SIU), 1–4.
  • Pervan, N. ve Keleş, H. Y. (2017). Sentiment Analysis Using A Random Forest Classifier On Turkish Web Comments. Communications Faculty Of Science University of Ankara, 59, 69–79.
  • Hayran, A. ve Sert, M. (2017). Sentiment Analysis on Microblog Data Based on Word Embedding and Fusion Techniques. In 2017 25th Signal Processing and Communications Applications Conference (SIU), 1–4.
  • Onan, A. (2017). “Twitter Mesajları Üzerinde Makine Öğrenmesi Yöntemlerine Dayalı Duygu Analizi. Yönetim Bilişim Sistemleri Dergisi, 3, 1–14.
  • Ding, Y., Li, B., Zhao, Y. ve Cheng, C. (2017). Scoring tourist attractions based on sentiment lexicon. In 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 1990–1993.
  • Kaynar, O., Aydın, Z. ve Görmez, Y. (2017). Sentiment Analizinde Öznitelik Düşürme Yöntemlerinin Oto Kodlayıcılı Derin Öğrenme Makinaları ile Karşılaştırılması. Bilişim Teknolojileri Dergisi, 10, 319–326.
  • Hassan, A. ve Mahmood, A. (2017). Deep Learning approach for sentiment analysis of short texts. In 2017 3rd international conference on control, automation and robotics (ICCAR), 705–710.
  • Cliche, M. (2017). BB twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs.
  • Atalay, M. ve Çelik, E. (2017). Büyük Veri Anali̇zi̇nde Yapay Zekâ Ve Maki̇ne Öğrenmesi̇ Uygulamaları - Artificial Intelligence and Machine Learning Applications in Big Data Analysis. Mehmet Akif Ersoy Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 9, 155–172.
  • Witten, I.H. ve Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques.
  • Chakraborty, K., Bag, R. ve Bhattacharyya, S. (2018). Relook into Sentiment Analysis performed on Indian Languages using Deep Learning. In 2018 Fourth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), 208–213.
  • Chen, Y. ve Zhang, Z. (2018). Research on text sentiment analysis based on CNNs and SVM. Proc. 13th IEEE Conf. Ind. Electron. Appl. ICIEA 2018, 2731–2734.
There are 62 citations in total.

Details

Primary Language Turkish
Subjects Computer Software
Journal Section Reviews
Authors

Hatice Elif Ekim 0000-0001-6623-5847

A. Burak İnner 0000-0003-0933-654X

Publication Date June 2, 2021
Submission Date November 1, 2020
Published in Issue Year 2021Volume: 24 Issue: 2

Cite

APA Ekim, H. E., & İnner, A. B. (2021). DUYGU ANALİZİ VE FİKİR MADENCİLİĞİ UYGULAMALARI ÜZERİNE LİTERATÜR TARAMASI. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 24(2), 93-114. https://doi.org/10.17780/ksujes.819367
 Download Cover Image