6th International Young Scientist Congress (IYSC-2020) will be Postponed to 8th and 9th May 2021 Due to COVID-19. 10th International Science Congress (ISC-2020).  International E-publication: Publish Projects, Dissertation, Theses, Books, Souvenir, Conference Proceeding with ISBN.  International E-Bulletin: Information/News regarding: Academics and Research

Hyper-parameter optimization: towards practical sentiment analysis using a Convolutional Neural Network (CNN)

Author Affiliations

  • 1Kirinyaga University, Kerugoya, Kenya

Res. J. Computer & IT Sci., Volume 7, Issue (2), Pages 1-5, December,20 (2019)

Abstract

Current advancements of in Deep Neural Networks (DNNs) has made it possible to assess the latent and sentimental polarity in text. This is helpful in many applications such as hate speech recognition and mood determination. Sentiment analysis, as it is popularly known in machine learning, is a typical task in text classification – an active research area of Natural Language Processing (NLP). One of the main challenges in machine learning is the ability to create accurate models applicable to real-life problems. In this paper, we report on the experimental results of hyper-parameter optimization of a Convolution Neural Network (CNN) – one of the most promising deep machine learning architecture for text processing as an attempt to move towards practical sentiment analysis models. The results indicate remarkable improvements over ordinary CNNs on typical machine learning datasets.

References

  1. Tang D., Qin B. and Liu T. (2015)., Deep learning for sentiment analysis: successful approaches and future challenges., Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 5(6), 292-303. doi: 10.1002/widm.1171. Retrieved from https://onlinelibrary.wiley.com/doi/pdf/10.1002/ widm.1171
  2. Ain Q.T., Ali M., Riaz A., Noureen A., Kamran M., Hayat B. and Rehman A. (2017)., Sentiment analysis using deep learning techniques: a review., Int J Adv Comput Sci Appl, 8(6), 424. Retrieved from https://thesai.org/Downloads/Volume8No6/Paper_57-Sentiment_Analysis_using_Deep_Learning.pdf
  3. Prajwal S. (2019)., Sentiment analysis for text with Deep Learning., Towards Data Science. Retrieved from https://towardsdatascience.com/sentiment-analysis-for-text-with-deep-learning-2f0a0c6472b5
  4. Zainab H., Muneera A., Nora A., Latifah A. and Sarah A. (2019)., Arabic Sentiment Analysis Using Deep Learning: A Review., IJCSNS International Journal of Computer Science and Network Security, 19(4), 255. Retrieved from http://paper.ijcsns.org/07_book/201904/20190435.pdf
  5. Vani K. and Alessandro A. (2019)., NOVEL2GRAPH: Visual Summaries of Narrative Text Enhanced by Machine Learning., In Proceedings of the Text2StoryIR′19 Workshop, Cologne, Germany, published at http://ceur-ws.org
  6. Yeshiwas G. and Abebe A. (2018)., Deep learning approach for amharic sentiment analysis university of gondar., Retrieved from: https://www.researchgate.net/publication/331673959
  7. Jaspreet S., Gurvinder S. and Rajinder S. (2017)., Optimization of sentiment analysis using machine learning classifers., Human-Centric Computing & Information Sciences, 7(1), 32. DOI 10.1186/s13673-017-0116-3. Retrieved from: https://link.springer.com/content/pdf/10.1186%2Fs13673-017-0116-3.pdf.
  8. Samuel P. (2018)., Sentiment analysis: Machine-Learning approach., Data Driven Investor. Retrieved from https://medium.com/datadriveninvestor/sentiment-analysis-machine-learning-approach-83e4ba38b57
  9. Munir A., Shabib A., Muhammad S.B., Noureen H., Iftikhar A. and Zahid N. (2018)., SVM Optimization for Sentiment Analysis., (IJACSA) International Journal of Advanced Computer Science and Applications, 9(4), Retrieved from https://pdfs.semanticscholar.org/3c9d/9cc4c4aad89ec00961efd76ec92c9fcbd4d2.pdf
  10. Ray P. and Chakrabarti A. (2019)., A Mixed approach of Deep Learning method and Rule-Based method to improve Aspect Level Sentiment Analysis., Applied Computing and Informatics. Retrieved from https://doi.org/10.1016/ j.aci.2019.02.002
  11. Md S.A., Ayush K., Asif E. and Pushpak B. (2016)., A Hybrid Deep Learning Architecture for Sentiment Analysis., Proceedings of COLING 2016. In the 26th International Conference on Computational Linguistics: Technical Papers, Osaka, Japan, 482-493, 11-17. Retrieved from https://www.aclweb.org/anthology/C16-1047.
  12. Chakravarthy A., Desai P., Deshmukh S., Gawande S. and Saha I. (2018)., Hybrid Architecture for Sentiment Analysis Using Deep Learning., International Journal of Advanced Research in Computer Science, 9(1), DOI: http://dx.doi.org/10.26483/ijarcs.v9i1.5388 Volume 9, No. 1. Available Online at www.ijarcs.info
  13. Komalpreet K., Chitender K. and Tarandeep K.B. (2019)., An optimized CNN based robust sentiment analysis system on big social data using text polarity feature., International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, 8(6). https://www.ijitee.org/wp-content/uploads/papers/ v8i6/F3928048619.pdf
  14. Tianyang Z., Minlie H. and Li Z. (2017)., Learning Structured Representation for Text Classification via Reinforcement Learning., Association for the Advancement of Artificial Intelligence https://www.microsoft.com/en-us/research/wp-content/uploads/2017/11/zhang.pdf
  15. Apostol V. (2019)., BowTie – A deep learning feedforward neural network for sentiment analysis., NIST Cybersecurity White Paper. available from: https://doi.org/10.6028/NIST. CSWP.04222019
  16. Juergen S. (2015)., Deep learning in neural networks: An overview., In Neural Networks., 61, 85-117. Retrieved from https://arxiv.org/abs/1404.7828