Sentiment Analysis from Tweets using Convolutional Neural Networks

Authors

  • Mesut Pek Istanbul Sisli Vocational School, Turkey
  • Metin Turan Istanbul Commerce University, Turkey

DOI:

https://doi.org/10.48149/jciees.2021.1.1.2

Keywords:

Sentiment Analysis, Twitter Sentiment Analysis, Deep Learning, Convolutional Neural Network

Abstract

By current improvements of web technology nowadays, usage of social media has increased. Twitter is a web site where millions share their opinions. Political parties, firms and other establishments has been examining data at these social media sites to learn person’s opinions about themselves. Reporting the sharing of millions of persons instantly is done more easily by using machine and deep learning techniques. In this work, sentiment analysis is done by the Convolutional Neural Network which has wide-spread usage in deep learning. Besides other known works, improvements in feature selection have been applied in order to meet higher success rate. Model has been trained by the different data sets and tested in other data sets. The model has reached to 97% success rate by the training data. 90% and 89% success rates have been achieved on the tests applied to other data sets.

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References

Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., & Yang, G. Z. (2007), Deep learning for health informatics, IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 1, 4-21, doi: 10.1109/JBHI.2016.2636665

Bengio, Y. (2009). Learning Deep Architectures for AI. Found. Trends Mach. Learn. Vol. 2, no. 1, 1-127, https://doi.org/10.1561/2200000006

Lee, D. H. (2013). Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. CML 2013 Workshop: Challenges in Representation Learning (WREPL), Atlanta, Georgia, USA, 1-6.

Cho, Y. & Saul, L. K. (2009). Kernel methods for deep learning. 22nd International Conference on Neural Information Processing Systems (NIPS'09). Red Hook, NY, USA, 342-350.

LeCun, Y., Bengio, Y. & Hinton, G. (2015). Deep learning. Nature vol. 521, 436-444, https://doi.org/10.1038/nature14539

Collobert R. & Weston, J. (2008) A unified architecture for natural language processing: Deep neural networks with multitask learning”, 25th international conference on Machine learning (ICML '08), Helsinki, Finland, 160-167, https://doi.org/10.1145/1390156.1390177

Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H. & Ng, A. Y. (2011). Multimodal deep learning, 28th International Conference on Machine Learning, Washington, USA, 689-696.

Kim, Y. (2014). Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. DOI: 10.3115/v1/D14-1181

Lai, S., Xu, L., Liu, K., Zhao, J. (2015). Recurrent convolutional neural networks for text classification. Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI'15). AAAI Press, 2267-2273.

Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. ArXiv, abs/1207.0580.

Aizenberg I., Aizenberg N. & Vandewalle J. (2000). Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications. Springer-Verlag, https://doi.org/10.1007/978-1-4757-3115-6.

Pek M. & Turan M. (2019). Sentiment Analysis of Tweets Using Machine Learning, International Conference on Data Science, machine Learning and Statistics, Turkey, Van, 85-87.

Ciresan D., Meier U. & Schmidhuber J. (2011). Flexible, High Performance Convolutional Neural Networks for Image Classification. International Joint Conference on Artificial Intelligence IJCAI-2011. pp. 1237-1242. DOI: 10.5591/978-1-57735-516-8/IJCAI11-210.

Gao J., Deng L., Gamon M. & He X. (2014). Modeling interestingness with deep neural networks. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, 2-13, https://doi.org/10.3115/v1/D14-1002

Akgül E. S., Ertano C. & Diri B. (2016). Sentiment analysis with Twitter. Pamukkale University Journal of Engineering Sciences, vol. 22 no. 2, 106-110. (in turkish).

Chen T., Xu R., He Y., & Wang X. (2016). Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN. Expert Systems with Applications, Elsevier, Vol. 72, 221-230, https://doi.org/10.1016/j.eswa.2016.10.065

Larsson, M., & Nilsson, A. (2017). Manifold Traversal for Reversing the Sentiment of Text.

Singh P., Sawhney R.S. & Kahlon K.S. (2017). Forecasting the 2016 US Presidential Elections Using Sentiment Analysis. In: Kar A. et al. (eds) Digital Nations – Smart Cities, Innovation, and Sustainability. I3E 2017. Lecture Notes in Computer Science, vol. 10595. Springer, Cham. https://doi.org/10.1007/978-3-319-68557-1_36

Şeker, Ş. E., Yeşilyurt A. (2017) Twitter Sentiment Analysis using Text Mining Methods. YBS Encyclopedia, vol. no.2, 26-36. (in turkish).

Bari A. & Saatçioğlu G., (2018). Emotion Artificial Intelligence Derived from Ensemble Learning, 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE), 1763-1770, doi: 10.1109/TrustCom/BigDataSE.2018.00266

Yang X, Macdonald C., Ounis L. (2018). Using word embeddings in Twitter election classification. Inf Retrieval J, vol. 21, pp. 183–207. https://doi.org/10.1007/s10791-017-9319-5

Liao S., Wang J., Yu R., Sato K. & Cheng Z. (2017). CNN for situations understanding based on sentiment analysis of twitter data. Procedia Computer Science, Vol. 111, 376-381, https://doi.org/10.1016/j.procs.2017.06.037

Salur M. U. & Aydın İ. (2018). The Impact of Preprocessing on Classification Performance in Convolutional Neural Networks for Turkish Text. International Conference on Artificial Intelligence and Data Processing (IDAP), Malatya, Turkey, 1-4, doi: 10.1109/IDAP.2018.8620722.

Zhao, L., & Zeng, C. (2017). Using Neural Networks to Predict Emoji Usage from Twitter Data.

Tantuğ, A. C. (2012). Metin Sınıflandırma, Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi.

Şeker, A., Diri, B. & Balik, H. (2017). Derin Öğrenme Yöntemleri ve Uygulamaları Hakkında Bir İnceleme, Gazi Mühendislik Bilimleri Dergisi.

Salakhutdinov, R., Mnih, A. & Hinton, G. (2007). Restricted Boltzmann machines for collaborative filtering, in Proceedings of the 24th international conference on Machine learning – ICML.

Pek, M. & Turan, M. (2019). Evrişimli Sinir Ağlari Kullanarak Imdb Film Yorumlarindan Duygu Analizi, Akademisyen Kitabevi A.Ş.

Pek, M. (2019). Ağlarda Saldiri Tespit Ve Önleme Sistemleri, Akademisyen Kitabevi A.Ş.

Boynukalın, Z. (2012). Emotion Analysis of Turkish Texts by Using Machine Learning Methods, MSc Thesis, Middle East Technical University.

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Published

2021-06-03

How to Cite

Pek, M., & Turan, M. (2021). Sentiment Analysis from Tweets using Convolutional Neural Networks. The Journal of CIEES, 1(1), 12–16. https://doi.org/10.48149/jciees.2021.1.1.2