Research Article
Year 2018,
Volume: 11 Issue: 3, 245 - 253, 31.07.2018
Abstract
Teknolojik ilerlemeler bilinen anlamdaki veri kavramını geliştirmekte ve
çeşitli tipte, büyük boyutlarda veri setlerini ulaşılabilir kılmaktadır.
Özellikle kişisel cihazların ve sosyal medya kanallarının yaygınlaşması ile
bireyler yaşamları ve duyguları hakkında çeşitli bilgiler paylaşmaktadırlar. Bu
da araştırmacılara anket gibi tekniklerle elde edilmesi güç boyutlarda veri
sağlayan kaynaklar oluşmasını sağlamaktadır. Metin verileri de hem sosyal medya
aracılığı ile en çok biriken veri çeşidinden biri olması hem de içerdiği
duygular bakımından önem taşımaktadır. Kişilerin duygularını ifade etmek veya
görüşlerini bildirmek amacıyla yazdıkları metinlerin analiz edilerek anlamlı
bilgilere dönüştürülmesi ve değer yaratılması veri analitiğinin amaçlarından birisidir.
Bu doğrultuda veri setinin analizi kadar çıktıların yorumlanarak değer
yaratılması da büyük önem taşımaktadır. Bu amaçla çalışmada çıktıları
yorumlayabilecek fakat yazılım altyapısı güçlü olmayan araştırmacıların da
kolaylıkla faydalanabileceği bir model önerilmiştir. Böylece çıktıların değer
bulacağı işletme, ekonomi ve sosyoloji gibi sosyal bilimler alanlarından
araştırmacıların gelişen teknoloji ve veri setlerini değerlendirmesi
hedeflenmiştir. Yarı denetimli öğrenme ile pozitif ve negatif duygu sözlükleri
genişletilmiş sözlük temelli duygu analizi tekniği kullanılan ve performansı
yüksek, uygulama anlamında çeşitli düzeyde yazılım bilgisine sahip kişilere
hitap eden bir duygu analizi modeli sunulmuştur.
References
- 1. Pang, B., Lee, L. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales, In Proceedings of the 43rd annual meeting on association for computational linguistics, Association for Computational Linguistics, 115-124, 2005. 2. Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., Kappas, A. Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61(12), 2544-2558, 2010. 3. Neviarouskaya, A., Prendinger, H., Ishizuka, M. Textual affect sensing for sociable and expressive online communication. Affective Computing and Intelligent Interaction, 218-229, 2007. 4. Hmeidi, I., Al-Ayyoub, M., Abdulla, N. A., Almodawar, A. A., Abooraig, R., Mahyoub, N. A. Automatic Arabic text categorization: A comprehensive comparative study. Journal of Information Science, 2014. 5. Weichselbraun, A., Gindl, S., Scharl, A. Enriching semantic knowledge bases for opinion mining in big data applications. Knowledge-based systems, 69, 78-85, 2014. 6. Meral, M., Dırı, B. Sentiment analysis on Twitter. In: Signal Processing and Communications Applications Conference (SIU), 2014 22nd. IEEE, 690-693, 2014. 7. Çoban, Ö., Özyer, B., Özyer, G. T. Sentiment analysis for Turkish Twitter feeds. In: Signal Processing and Communications Applications Conference (SIU), 23th. IEEE, 2388-2391, 2015. 8. Ahkter, J. K., Soria, S. Sentiment analysis: Facebook status messages. Unpublished master's thesis, Stanford, CA, 2010. 9. Gunawardena, N., Plumb, J., Xiao, N., Zhang, H. Instagram hashtag sentiment analysis. In University of Utah CS530/CS630 Conference of Machine Learning, 2013. 10. Kang D. ve Park Y. Review-based measurement of customer satisfaction in mobile Service: Sentiment analysis and VIKOR approach. Expert Systems with Applications 41, 1041-1050, 2014. 11. Jang H. J., Sim J., Lee Y. Ve Kwon O. Deep sentiment analysis: Mining the causality between personality-value-attitude for analyzing business ads in social media. Expert Systems with Applications 40, 7492-7503, 2013. 12. He, W., Zha, S., Li, L. Social media competitive analysis and text mining: A case study in the pizza industry. International Journal of Information Management, 33(3), 464-472, 2013. 13. Mostafa, M. M. More than words: Social networks’ text mining for consumer brand sentiments. Expert Systems with Applications, 40(10), 4241-4251, 2013. 14. Zheng X., Zhu S. ve Li Z. Capturing the essence of word-of-mouth for social commerce: Assessing the quality of online e-commerce reviews by a semi-supervised approach. Decision Support Systems 56, 211-222, 2013. 15. Eirinaki, M., Pisal, S., Singh, J. Feature-based opinion mining and ranking. Journal of Computer and System Sciences, 78(4), 1175-1184, 2012. 16. Zhang, X., Fuehres, H., Gloor, P. A. Predicting stock market indicators through twitter “I hope it is not as bad as I fear”. Procedia-Social and Behavioral Sciences, 26, 55-62, 2011. 17. Bollen, J., Mao, H., Zeng, X. Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1-8, 2011. 18. Mittal, A., Goel, A. Stock prediction using twitter sentiment analysis. Standford University, CS229, 2012. 19. Bouktif, S., Awad, M. A. Ant colony based approach to predict stock market movement from mood collected on Twitter. In Advances in Social Networks Analysis and Mining (ASONAM), 2013 IEEE/ACM International Conference, 837-845, 2013. 20. Porshnev, A., Redkin, I., Shevchenko, A. Machine learning in prediction of stock market indicators based on historical data and data from twitter sentiment analysis. In 2013 IEEE 13th International Conference on Data Mining Workshops, 440-444, 2013. 21. Elıaçık, A.B., Erdogan, N. Mikro Bloglardaki Finans Toplulukları için Kullanıcı Ağırlıklandırılmış Duygu Analizi Yöntemi. In: UYMS. 2015. 22. Corea, F. Can Twitter Proxy the Investors' Sentiment? The Case for the Technology Sector. Big Data Research, 2016. 23. Stieglitz, S., Dang-Xuan, L., Bruns, A., Neuberger, C. Social media analytics. Wirtschaftsinformatik, 56(2), 101-109, 2014. 24. XIA, R., ZONG, C., LI, S. Ensemble of feature sets and classification algorithms for sentiment classification. Information Sciences, 181.6: 1138-1152, 2011. 25. Rahman, M. N., Esmailpour, A., Zhao, J. Machine Learning with Big Data An Efficient Electricity Generation Forecasting System. Big Data Research, 5, 9-15, 2016. 26. LIU, B. Sentiment analysis and opinion mining: Synthesis lectures on human language technologies [M].[sl]: Morgan Claypool Publishers, 1–167, 2012. 27. Medhat, W., Hassan, A., Korashy, H. Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093-1113, 2014. 28. Catal, C., Nangir, M. A sentiment classification model based on multiple classifiers. Applied Soft Computing, 50, 135-141, 2017. 29. Liu, B. Sentiment analysis and subjectivity. In Handbook of Natural Language Processing, Second Edition, Chapman and Hall/CRC, 627-666, 2010. 30. Zhang, L. Sentiment analysis on Twitter with stock price and significant keyword correlation (Doctoral dissertation), 2013.
Year 2018,
Volume: 11 Issue: 3, 245 - 253, 31.07.2018
Abstract
References
- 1. Pang, B., Lee, L. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales, In Proceedings of the 43rd annual meeting on association for computational linguistics, Association for Computational Linguistics, 115-124, 2005. 2. Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., Kappas, A. Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61(12), 2544-2558, 2010. 3. Neviarouskaya, A., Prendinger, H., Ishizuka, M. Textual affect sensing for sociable and expressive online communication. Affective Computing and Intelligent Interaction, 218-229, 2007. 4. Hmeidi, I., Al-Ayyoub, M., Abdulla, N. A., Almodawar, A. A., Abooraig, R., Mahyoub, N. A. Automatic Arabic text categorization: A comprehensive comparative study. Journal of Information Science, 2014. 5. Weichselbraun, A., Gindl, S., Scharl, A. Enriching semantic knowledge bases for opinion mining in big data applications. Knowledge-based systems, 69, 78-85, 2014. 6. Meral, M., Dırı, B. Sentiment analysis on Twitter. In: Signal Processing and Communications Applications Conference (SIU), 2014 22nd. IEEE, 690-693, 2014. 7. Çoban, Ö., Özyer, B., Özyer, G. T. Sentiment analysis for Turkish Twitter feeds. In: Signal Processing and Communications Applications Conference (SIU), 23th. IEEE, 2388-2391, 2015. 8. Ahkter, J. K., Soria, S. Sentiment analysis: Facebook status messages. Unpublished master's thesis, Stanford, CA, 2010. 9. Gunawardena, N., Plumb, J., Xiao, N., Zhang, H. Instagram hashtag sentiment analysis. In University of Utah CS530/CS630 Conference of Machine Learning, 2013. 10. Kang D. ve Park Y. Review-based measurement of customer satisfaction in mobile Service: Sentiment analysis and VIKOR approach. Expert Systems with Applications 41, 1041-1050, 2014. 11. Jang H. J., Sim J., Lee Y. Ve Kwon O. Deep sentiment analysis: Mining the causality between personality-value-attitude for analyzing business ads in social media. Expert Systems with Applications 40, 7492-7503, 2013. 12. He, W., Zha, S., Li, L. Social media competitive analysis and text mining: A case study in the pizza industry. International Journal of Information Management, 33(3), 464-472, 2013. 13. Mostafa, M. M. More than words: Social networks’ text mining for consumer brand sentiments. Expert Systems with Applications, 40(10), 4241-4251, 2013. 14. Zheng X., Zhu S. ve Li Z. Capturing the essence of word-of-mouth for social commerce: Assessing the quality of online e-commerce reviews by a semi-supervised approach. Decision Support Systems 56, 211-222, 2013. 15. Eirinaki, M., Pisal, S., Singh, J. Feature-based opinion mining and ranking. Journal of Computer and System Sciences, 78(4), 1175-1184, 2012. 16. Zhang, X., Fuehres, H., Gloor, P. A. Predicting stock market indicators through twitter “I hope it is not as bad as I fear”. Procedia-Social and Behavioral Sciences, 26, 55-62, 2011. 17. Bollen, J., Mao, H., Zeng, X. Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1-8, 2011. 18. Mittal, A., Goel, A. Stock prediction using twitter sentiment analysis. Standford University, CS229, 2012. 19. Bouktif, S., Awad, M. A. Ant colony based approach to predict stock market movement from mood collected on Twitter. In Advances in Social Networks Analysis and Mining (ASONAM), 2013 IEEE/ACM International Conference, 837-845, 2013. 20. Porshnev, A., Redkin, I., Shevchenko, A. Machine learning in prediction of stock market indicators based on historical data and data from twitter sentiment analysis. In 2013 IEEE 13th International Conference on Data Mining Workshops, 440-444, 2013. 21. Elıaçık, A.B., Erdogan, N. Mikro Bloglardaki Finans Toplulukları için Kullanıcı Ağırlıklandırılmış Duygu Analizi Yöntemi. In: UYMS. 2015. 22. Corea, F. Can Twitter Proxy the Investors' Sentiment? The Case for the Technology Sector. Big Data Research, 2016. 23. Stieglitz, S., Dang-Xuan, L., Bruns, A., Neuberger, C. Social media analytics. Wirtschaftsinformatik, 56(2), 101-109, 2014. 24. XIA, R., ZONG, C., LI, S. Ensemble of feature sets and classification algorithms for sentiment classification. Information Sciences, 181.6: 1138-1152, 2011. 25. Rahman, M. N., Esmailpour, A., Zhao, J. Machine Learning with Big Data An Efficient Electricity Generation Forecasting System. Big Data Research, 5, 9-15, 2016. 26. LIU, B. Sentiment analysis and opinion mining: Synthesis lectures on human language technologies [M].[sl]: Morgan Claypool Publishers, 1–167, 2012. 27. Medhat, W., Hassan, A., Korashy, H. Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093-1113, 2014. 28. Catal, C., Nangir, M. A sentiment classification model based on multiple classifiers. Applied Soft Computing, 50, 135-141, 2017. 29. Liu, B. Sentiment analysis and subjectivity. In Handbook of Natural Language Processing, Second Edition, Chapman and Hall/CRC, 627-666, 2010. 30. Zhang, L. Sentiment analysis on Twitter with stock price and significant keyword correlation (Doctoral dissertation), 2013.
There are 1 citations in total.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Computer Software |
| Journal Section | Articles |
| Authors | |
| Publication Date | July 31, 2018 |
| Submission Date | October 9, 2017 |
| Published in Issue | Year 2018 Volume: 11 Issue: 3 |
