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Complexity Matrices in Twitter Sentiment Analysis of Thoughts on Mobile Games Using Machine Learning Algorithms

Year 2021, Volume: 2 Issue: 2, 91 - 100, 29.12.2021

Abstract

In modern times, people have started sharing their opinions, thoughts and feelings with other people through social media. The growing number of social media users and their share in it has naturally drawn the attention of researchers to this field. Twitter is one of the leading data sources in this field. Since Twitter has millions of users from different cultures and classes, it is possible to collect comments in different languages and content. Tweets that people write and share in 280 characters are used for research and analysis. Considering the fact that not all tweets can be read by people, in this study, sentiment analysis was performed using naive bayes (NB) classification algorithm and multilayer artificial neural networks (ML-ANN) based on the content of comments on mobile games. As a result of the analysis, it was found that multilayer artificial neural networks gave better results than the other methods on both training and test data.

References

  • 1. Go, A., Huang, L., & Bhayani, R. (2009). Twitter sentiment analysis. Entropy, 17, 252.
  • 2. Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams engineering journal, 5(4), 1093-1113.
  • 3. Yi, J., Nasukawa, T., Bunescu, R., & Niblack, W. (2003, November). Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. In Third IEEE international conference on data mining (pp. 427-434).
  • 4. Kaya, M., Fidan, G., & Toroslu, I. H. (2012, December). Sentiment analysis of Turkish political news. In 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (Vol. 1, pp. 174-180).
  • 5. Çoban, Ö., & Özyer, G. T. (2016, May). Sentiment classification for Turkish Twitter feeds using LDA. In 2016 24th Signal Processing and Communication Application Conference (SIU) (pp. 129-132).
  • 6. Akın, A. A., & Akın, M. D. (2007). Zemberek, an open source nlp framework for turkic languages. Structure, 10(2007), 1-5.
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  • 8. Katz, G., Ofek, N., & Shapira, B. (2015). ConSent: Context-based sentiment analysis. Knowledge-Based Systems, 84, 162-178.
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  • 13. Santos, R. L. D. S., de Sousa, R. F., Rabelo, R. A., & Moura, R. S. (2016, July). An experimental study based on fuzzy systems and artificial neural networks to estimate the importance of reviews about product and services. In 2016 International Joint Conference on Neural Networks (IJCNN) (pp. 647-653).
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  • 18. Olgun, M., & Özdemir, G. (2012). Istatistiksel Özellik Temelli Bayes Siniflandirici Kullanarak Kontrol Grafiklerinde Örüntü Tanima. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 27(2).
  • 19. Bustami, R., Bessaih, N., Bong, C., & Suhaili, S. (2007). Artificial Neural Network for Precipitation and Water Level Predictions of Bedup River. IAENG International Journal of computer science, 34(2).
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  • 22. O. Kaynar ve S. Yıldız. “İstenmeyen Elektronik Postaların Centroid Tabanlı Sınıflayıcı, Yapay Sinir Ağları ve Destek Vektör Makinaları Yardımıyla Tespit Edilmesi,”, 2. Ulusal Yönetim Bilimleri Kongresi, Türkiye, 8-10 Ekim 2015, cilt.1, ss.144-153.
  • 23. Ulusoy, T. (2010). İmkb Endeks Öngörüsü İçi̇n İleri̇ Beslemeli̇ Ağ Mi̇mari̇si̇ne Sahi̇p Yapay Si̇ni̇r Aği Modellemesi̇. Uluslararası İktisadi ve İdari İncelemeler Dergisi, (5)

Complexity Matrices in Twitter Sentiment Analysis of Thoughts on Mobile Games Using Machine Learning Algorithms

Year 2021, Volume: 2 Issue: 2, 91 - 100, 29.12.2021

Abstract

Günümüzde insanlar sosyal medya aracılığıyla fikir, düşünce ve duygularını diğer insanlarla paylaşmaya başladılar. Artan sosyal medya kullanıcıları ve paylaşımları, doğal olarak araştırmacıların dikkatini bu alana çekmiştir. Twitter bu alanda önde gelen veri kaynaklarından biridir. Twitter'ın farklı kültür ve sınıflardan milyonlarca kullanıcısı olduğu için farklı dillerde ve içeriklerde yorum toplamak mümkündür. İnsanların 280 karakterde yazıp paylaştığı tweetler araştırma ve analiz için kullanılmaktadır. Tüm tweetlerin insanlar tarafından okunamayacağı gerçeğinden hareketle bu çalışmada, mobil oyunlara yapılan yorumların içeriğine dayalı olarak naive bayes (NB) sınıflandırma algoritması ve çok katmanlı yapay sinir ağları (ML-ANN) kullanılarak duygu analizi yapılmıştır. Analiz sonucunda çok katmanlı yapay sinir ağlarının hem eğitim hem de test verileri üzerinde diğer yöntemlere göre daha iyi sonuçlar verdiği tespit edilmiştir.

References

  • 1. Go, A., Huang, L., & Bhayani, R. (2009). Twitter sentiment analysis. Entropy, 17, 252.
  • 2. Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams engineering journal, 5(4), 1093-1113.
  • 3. Yi, J., Nasukawa, T., Bunescu, R., & Niblack, W. (2003, November). Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. In Third IEEE international conference on data mining (pp. 427-434).
  • 4. Kaya, M., Fidan, G., & Toroslu, I. H. (2012, December). Sentiment analysis of Turkish political news. In 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (Vol. 1, pp. 174-180).
  • 5. Çoban, Ö., & Özyer, G. T. (2016, May). Sentiment classification for Turkish Twitter feeds using LDA. In 2016 24th Signal Processing and Communication Application Conference (SIU) (pp. 129-132).
  • 6. Akın, A. A., & Akın, M. D. (2007). Zemberek, an open source nlp framework for turkic languages. Structure, 10(2007), 1-5.
  • 7. Pennacchiotti, M., & Popescu, A. M. (2011, July). A machine learning approach to twitter user classification. In Fifth international AAAI conference on weblogs and social media.
  • 8. Katz, G., Ofek, N., & Shapira, B. (2015). ConSent: Context-based sentiment analysis. Knowledge-Based Systems, 84, 162-178.
  • 9. Nikfarjam, A., Sarker, A., O’connor, K., Ginn, R., & Gonzalez, G. (2015). Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features. Journal of the American Medical Informatics Association, 22(3), 671-681.
  • 10. Nizam, H., & Akın, S. S. (2014). Sosyal medyada makine öğrenmesi ile duygu analizinde dengeli ve dengesiz veri setlerinin performanslarının karşılaştırılması. XIX. Türkiye'de İnternet Konferansı, 1-6.
  • 11. Türkmen, A. C., & Cemgil, A. T. (2014, April). Political interest and tendency prediction from microblog data. In 2014 22nd Signal Processing and Communications Applications Conference (SIU) (pp. 1327-1330).
  • 12. Wang, W. (2010, August). Sentiment analysis of online product reviews with Semi-supervised topic sentiment mixture model. In 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery (Vol. 5, pp. 2385-2389)
  • 13. Santos, R. L. D. S., de Sousa, R. F., Rabelo, R. A., & Moura, R. S. (2016, July). An experimental study based on fuzzy systems and artificial neural networks to estimate the importance of reviews about product and services. In 2016 International Joint Conference on Neural Networks (IJCNN) (pp. 647-653).
  • 14. Anjaria, M., & Guddeti, R. M. R. (2014, January). Influence factor-based opinion mining of Twitter data using supervised learning. In 2014 Sixth International Conference on Communication Systems and Networks (COMSNETS) (pp. 1-8).
  • 15. Neethu, M. S., & Rajasree, R. (2013, July). Sentiment analysis in twitter using machine learning techniques. In 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT) (pp. 1-5).
  • 16. Nurwidyantoro, A. (2016, October). Sentiment analysis of economic news in Bahasa Indonesia using majority vote classifier. In 2016 International Conference on Data and Software Engineering (ICoDSE) (pp. 1-6).
  • 17. Uçar, M. K., & Topal, İ. Yapay Sinir Ağları ile Reklam Sektöründe Kullanıcı Profili Çıkarma Uygulaması: Çin–Türkiye Örneği. Veri Bilimi, 1(1), 20-28.
  • 18. Olgun, M., & Özdemir, G. (2012). Istatistiksel Özellik Temelli Bayes Siniflandirici Kullanarak Kontrol Grafiklerinde Örüntü Tanima. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 27(2).
  • 19. Bustami, R., Bessaih, N., Bong, C., & Suhaili, S. (2007). Artificial Neural Network for Precipitation and Water Level Predictions of Bedup River. IAENG International Journal of computer science, 34(2).
  • 20. Raza, G. M., Butt, Z. S., Latif, S., & Wahid, A. (2021, May). Sentiment Analysis on COVID Tweets: An Experimental Analysis on the Impact of Count Vectorizer and TF-IDF on Sentiment Predictions using Deep Learning Models. In 2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) (pp. 1-6).
  • 21. Tripathy, A., Agrawal, A., & Rath, S. K. (2016). Classification of sentiment reviews using n-gram machine learning approach. Expert Systems with Applications, 57, 117-126.
  • 22. O. Kaynar ve S. Yıldız. “İstenmeyen Elektronik Postaların Centroid Tabanlı Sınıflayıcı, Yapay Sinir Ağları ve Destek Vektör Makinaları Yardımıyla Tespit Edilmesi,”, 2. Ulusal Yönetim Bilimleri Kongresi, Türkiye, 8-10 Ekim 2015, cilt.1, ss.144-153.
  • 23. Ulusoy, T. (2010). İmkb Endeks Öngörüsü İçi̇n İleri̇ Beslemeli̇ Ağ Mi̇mari̇si̇ne Sahi̇p Yapay Si̇ni̇r Aği Modellemesi̇. Uluslararası İktisadi ve İdari İncelemeler Dergisi, (5)
There are 23 citations in total.

Details

Primary Language English
Subjects Software Engineering
Journal Section Research Articles
Authors

Erol Kına 0000-0002-7785-646X

Recep Özdağ 0000-0001-5247-5591

Publication Date December 29, 2021
Submission Date December 13, 2021
Published in Issue Year 2021 Volume: 2 Issue: 2

Cite

APA Kına, E., & Özdağ, R. (2021). Complexity Matrices in Twitter Sentiment Analysis of Thoughts on Mobile Games Using Machine Learning Algorithms. Muş Alparslan Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 2(2), 91-100.
AMA Kına E, Özdağ R. Complexity Matrices in Twitter Sentiment Analysis of Thoughts on Mobile Games Using Machine Learning Algorithms. Muş Alparslan Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi. December 2021;2(2):91-100.
Chicago Kına, Erol, and Recep Özdağ. “Complexity Matrices in Twitter Sentiment Analysis of Thoughts on Mobile Games Using Machine Learning Algorithms”. Muş Alparslan Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 2, no. 2 (December 2021): 91-100.
EndNote Kına E, Özdağ R (December 1, 2021) Complexity Matrices in Twitter Sentiment Analysis of Thoughts on Mobile Games Using Machine Learning Algorithms. Muş Alparslan Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 2 2 91–100.
IEEE E. Kına and R. Özdağ, “Complexity Matrices in Twitter Sentiment Analysis of Thoughts on Mobile Games Using Machine Learning Algorithms”, Muş Alparslan Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol. 2, no. 2, pp. 91–100, 2021.
ISNAD Kına, Erol - Özdağ, Recep. “Complexity Matrices in Twitter Sentiment Analysis of Thoughts on Mobile Games Using Machine Learning Algorithms”. Muş Alparslan Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 2/2 (December 2021), 91-100.
JAMA Kına E, Özdağ R. Complexity Matrices in Twitter Sentiment Analysis of Thoughts on Mobile Games Using Machine Learning Algorithms. Muş Alparslan Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi. 2021;2:91–100.
MLA Kına, Erol and Recep Özdağ. “Complexity Matrices in Twitter Sentiment Analysis of Thoughts on Mobile Games Using Machine Learning Algorithms”. Muş Alparslan Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol. 2, no. 2, 2021, pp. 91-100.
Vancouver Kına E, Özdağ R. Complexity Matrices in Twitter Sentiment Analysis of Thoughts on Mobile Games Using Machine Learning Algorithms. Muş Alparslan Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi. 2021;2(2):91-100.