A Review on Sentiment Analysis of Opinion Mining

  • Sireesha JastiEmail author
  • Tummala Sita Mahalakshmi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 768)


In the recent era of Internet, social network plays very important role and occupies majority of share in data sharing between various groups. The data in social sites contain multidimensional data posted by different types of people. The posting contain people observations, thoughts, opinions, decisions and the rationale behind those decisions. Based on these postings or tweets one can analyse the sentiment about that specific product, service, event or any other participating by sharing their opinions, activity thoughts and ideas. In this paper, efficient algorithms are discussed for sentiment analysis of the tweets. The opinion on a specific topic mainly depends on the people, also the accuracy of opinions mining depends on the polarity strength. In this paper various Machine learning algorithms and various pre-processing techniques that make the data ready for opinion mining are discussed.


Opinion mining Sentiment analysis Social media Internet Feature extraction Tweets Filtering Pre-processing 


  1. 1.
    Khan, K., Baharudin, B., Khan, A., Ullah, A.: Mining Opinion Components from Unstructured Reviews: A Review, pp. 1319–1578 (2014/2012)Google Scholar
  2. 2.
    Bhatia S., et al.: Strategies for mining opinions: a survey. In: IEEE 2nd International Conference on Computing for Sustainable Global Development (INDIACom) (2015)Google Scholar
  3. 3.
    Cambria, Erik, et al.: New Avenues in Opinion Mining and Sentiment Analysis. Published by the IEEE Computer Society, IEEE Intelligent systems (2013)Google Scholar
  4. 4.
    Rao K.M., et al.: An efficient method for parameter estimation of software reliability growth model using artificial bee colony optimization. Lect. Notes Comput. Sci. (LNCS-Springer series), 8947, 765–776 (2015)Google Scholar
  5. 5.
    Kumari, N., Sentiment analysis on E-commerce application by using opinion mining. In: 6th International Conference—Cloud System and Big Data Engineering (2016)Google Scholar
  6. 6.
    Angiani, G., Ferrari, L., Fontanini, T., Fornacciari, P., Iotti, E., Magliani, F., Manicard, S.: A Comparison Between Preprocessing Techniques for Sentiment Analysis in Twitter, vol. 6, pp. 417–422 (2006)Google Scholar
  7. 7.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found Trends Inf Retrieval 2, 1–135 (2008)CrossRefGoogle Scholar
  8. 8.
    Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: LREC, 2010, pp. 1320–1326Google Scholar
  9. 9.
    Genkin, A., Lewis, D.D., Madigan, D.: Large-scale Bayesian logistic regression for text categorization. Technometrics 49, 291–304 (2007)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval (1986)Google Scholar
  11. 11.
    Zhang, L.: Sentiment Analysis on Twitter with Stock Price and Significant Keyword Correlation (2013)Google Scholar
  12. 12.
    Jivani, A.G.: A comparative study of stemming algorithms. Int. J. Comp. Tech. Appl 2, 1930–1938 (2011)Google Scholar
  13. 13.
    Vijayarani, S., Janani, R.: Text Mining: Open Source Tokenization Tools—An Analysis, vol. 3 (2016)Google Scholar
  14. 14.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs Up?: Sentiment Classification Using Machine Learning Techniques. In: Proceedings of the ACL-02 Conference on EMPIRICAL Methods in Natural Language Processing, vol. 10, pp. 79–86 (2002)Google Scholar
  15. 15.
    Domingos, P., Pazzani, M.: On the optimality of the simple Bayesian classifier under zero-one loss. Mach. Learn. 29, 103–130 (1997)CrossRefGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.GITAM UniversityVisakhapatnamIndia
  2. 2.Department of CSEMalla Reddy Engineering College (A)SecunderabadIndia

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