Machine Learning in Cloud: Sentiment Analyzing System


  • Roman Dyussembayev
  • Maryam Shahpasand
  • . .





Sentiment analyzing, Machine Learning, Cloud Computing, Natural language processing, Data Science


As the number of computer users increases, numerous content has been generated by them. Machine learning as one of the main direction of natural language processing, allows computer systems to extract various information from the generated content. Processing results determine the sentiments of the text to extract the author's emotional evaluation that is expressed in the text. The aim of the project was to develop the Sentiment Analyzing system by using Machine Learning algorithms on cloud-based system. The paper describes the development process of Sentiment Analyzing System in English language. Two Machine Learning algorithms, SVM and Naïve Bayes classifier, have been inspected and Cloud computing used to develop and publish web application. The testing results demonstrate the accuracy of the work in proposed method.





[1] Bollen J., Pepe A., Mao H., (2011). Modeling Public Mood and Emotion: Twitter Sentiment and Socio-Economic Phenomena. Barcelona, Fifth International AAAI Conference on Weblogs.

[2] Yussupova, Bogdanova, Boyko, 2014. Applying of Sentiment Analysis for Texts in Russian. Ufa, The Second International Conference on Advances in Information Mining and Management.

[3] Marouane B., 2017. Machine Learning and Semantic Sentiment Analysis based Algorithms for Suicide Sentiment Prediction in Social Networks. Procedia Computer Science, Volume 113, pp. 65-72.

[4] Pang B., Lee L., 2004. A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization. Ithaca, NY 14853-7501: Department of Computer Science, Cor-nell University.

[5] Pang B., Lee L., 2008. Opinion Mining and Sentiment. 2 ed. NY USA: Computer Science Department, Cornell University.

[6] Mehdi A., Seyedamin P., Saied S., Elizabeth D., Krys K. 2017. A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques. CoRR, Volume: abs/1707.02919.

[7] Prabowo R., Thelwall M., 2009. Sentiment analysis: A combined approach. School of Computing and Information Technology, 143-157(2), pp. 1-21.

[8] König A., Brill E., 2016. Reducing the Human Overhead in Text Categorization. Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ISBN: 978-1-60558-193-4, pp. 274-282.

[9] Joachims, T., 1998. Text categorization with Support Vector Machines: Learning with many relevant features. ECML 1998: Machine Learning: ECML-98, Issue 1398, pp. 137-142.

[10] Mohammad, S. M., 2015. Challenges in Sentiment Analysis. Montreal, Canada: National Research Council Canada.

[11] Rentoumi, Petrakis, Klenner, Vouros, Karkaletsis, 2010. United we stand: improving sentiment analysis by joining machine learning and rule-based methods. Malta, 7th International Conference on Language Resources and Evaluation.

[12] Ali H., Sana M., Ahmad K., Shahaboddin S. 2018, "Machine Learning-Based Sentiment Analysis for Twitter Accounts", Mathematical and Computational Applications 2018, 23(1), 11

[13] Devendra S., Manzil Z., Ruslan S. 2018, "Investigating the Working of Text Classifiers", CoRR, abs/1801.06261

[14] Alec Go, Richa Bhayani, Lei Huang, 2009. Twitter sentiment classification using distant supervision. pp. 1-6.

[15] Monireh E., Amir H., Amit S. 2017, "On the Challenges of Sentiment Analysis for Dynamic Events

[15]", IEEE Intelligent Systems, Volume: 32, Issue: 5, pp:70-75

[16] V.Uma Ramya, K. Thirupathi Rao 2018, "Sentiment Analysis of Movie Review using Machine Learning Techniques", International Journal of Engineering & Technology, 7 (2.7) 676-681

[17] P. Cook, S. Stevenson 2009, "An unsupervised model for text message normalization", NAACL HLT 2009 Workshop on Computational Approaches to Linguistic Creativity, 71-78, Boulder, Colorado.

[18] J. Wright, K. Leyton-Brown 2017, "Predicting Human Behavior in Unrepeated, Simultaneous-Move Games", Games Theory and Economic Behavior (GEB), volume 106, pp. 16-37

[19] J. Hartford, J. Wright, K. Leyton-Brown 2016, "Deep Learning for Predicting Human Strategic Behavior", Oral presentation at Conference on Neural Information Processing Systems (NIPS)

[20] J. Deriu, A. Lucchi, V. De Luca, A. Severyn, S. Müller, M. Cieliebak, T. Hofmann, M. Jaggi 2017, "Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification" In Proceedings of the 26th International Conference on World Wide Web

View Full Article:

How to Cite

Dyussembayev, R., Shahpasand, M., & ., . (2018). Machine Learning in Cloud: Sentiment Analyzing System. International Journal of Engineering & Technology, 7(4.40), 104–107.
Received 2018-12-19
Accepted 2018-12-19
Published 2018-12-16