Comparision of two sentiment analysis algorythms

  • 1 Department of Computer Science – University of Chemical Technology and Metallurgy, Bulgaria


This paper presents the comparison of the capabilities of two algorithms for Sentiment Analysis developed in Python. Both Python programs are used on the same Yelp dataset with customer reviews of the quality of the services in USA restaurants. The programs are based on open-source software frameworks and libraries as Python, NTLK, Scikit-Learn, Panda, etc. which are oriented to Machine and Learning and Natural Language Processing. The evaluation of the programs is based on precision of the predicted results and the compactness of the programming code. For model training and prediction, the Multinomial Naïve Bayes and Support Vectors Machines classifiers are applied in both algorithms.



  1. Hackelingm G.,Mastering Machine Learning with scikitlearn, 2014, Pakt Publishing
  2. Lane H., Howard C., Hapke H. M., Natural Language Processing in Action Understanding, analyzing, and generating text with Python, Manning Publications Co., Shelter Island, NY
  3. Palash, Goyal, Sumit, Pandey, Karan, Jain, Deep Learning for Natural Language Processing: Creating Neural Networks with Python, ISBN-13 (pbk): 978-1-4842-3684-0
  4. Chopra D., Joshi N., Mathur I., Mastering Natural Language Processing with Python, Copyright © 2016 Packt Publishing
  5. Ahmad M., Document Classification Using Python and Machine Learning, Digital Vidya, Dec. 2018,
  6. Ramesh R., Divya G., Divya D., Merin K., Vishnuprabha V., Big Data Sentiment Analysis using Hadoop, IJIRST, Volume 1, Issue 11, pp. 92-98, 2015.
  7. Zainuddin N., Selamat A., Sentiment Analysis Using Support Vector Machine, IEEE International Conference on Computer, Communication, and Control Technology (I4CT 2014), Kedah, Malaysia,pp.333-337, 2014.
  8. Mehta R., Big Data Analytics with Java, Packt Publishing Ltd, ISBN 978-78728-898-0, UK, 2017.
  9. Al-Barznji K., Atanassov., A Framework for Cloud Based Hybrid Recommender System for Big Data Mining, Journal of Science, Engineering & Education, Volume 2, Issue 1, UCTM, Sofia, Bulgaria, pp. 58-65, 2017.
  10. Guller M., Big Data Analytics with Spark, ISBN-13 (pbk): 978-1-4842-0965-3, 2015.
  11. Pentreath N., Machine Learning with Spark, Packt Publishing Ltd. Birmingham – Mumbai, 2015.
  12., Online Feb 2018.
  13. Fang X., Zhan J., Sentiment analysis using product review data, Joournal of Big Data, pp. 1–14, 2015.
  14. 7. Atanassov A., Al-Barznji K., Tomova F., System for Sentiment Analysis of Big Text Data, International virtual journal for science, techniques and innovation for the industry MTM, Issue 8, /2018 ISSN 1313-0226

Article full text

Download PDF