Comparative analysis of machine learning algorithms on social media test

  • Authors

    • R Ragupathy
    • Lakshmana Phaneendra Maguluri
    2018-03-19
    https://doi.org/10.14419/ijet.v7i2.8.10425
  • Sentimental Analysis, Social Reviews, Text Pre-processing, Sentiment Score, Machine Learning Techniques, Comparative Study

  • Abstract

    Sentiment analysis deals with identifying and classifying opinions or sentiments expressed in main text. It mainly refers to a text classification. Social media is generating a vast amount of sentiment rich data in the form of tweets, blog posts, comments, status updates, news etc. Sentiment analysis of this user generated data is very useful in knowing the opinion of the public. Knowledge base approach and Machine learning approach are the two strategies used for analyzing sentiments from the text. In this paper, Machine learning approach has been used for the sentiment analysis of movie review dataset and is analysed by Naïve Bayes, Decision tree, KNN, and SVM classifiers. Commencing the most efficient classification technique is the moto of the paper. Efficiency of the classifier is decided based on some regular parameters that are outputs of the classification techniques.

  • References

    1. [1] W. Medhat, A. Hassan and H. Korashy, "Sentiment analysis algorithms and applications: A survey", Ain Shams Engineering Journal, vol. 5, no. 4, pp. 1093-1113, 2014.

      [2] A. Deshwal and S. Sharma, "Twitter sentiment analysis using various classification algorithms", 2016 5th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 2016.

      [3] DoaaMohey El-Din , Hoda M.O. Mokhtar ,Osama Ismael, "Online Paper Review Analysis", (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 6, No. 9, 2015.

      [4] B. Liu, "Sentiment Analysis and Opinion Mining", Synthesis Lectures on Human Language Technologies, vol. 5, no. 1, pp. 1-167, 2012.

      [5] A. Jain and P. Dandannavar, "Application of machine learning techniques to sentiment analysis", 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (ICATCCT), 2016.

      [6] A. Pak, and P. Paroubek, "Twitter as a Corpus for Sentiment Analysis and Opinion Mining, " Special Issue of International Journal of Computer Application, France: Universida Paris-Sud, 2010.

      [7] DauméIII and D. Marcu ,"Domain adaptation for statistical classifiers", Journal of Artificial Intelligence Research, 26:101–126, 2006.

      [8] Jaap Kamps, Robert J. Mokken, Maarten Marx, and Maarten de Rijke, ‘Using WordNet to measure semantic orientation of adjectives’, in Pro- ceedings of the 4th International Conference on Language Resources and Evaluation (LREC 2004), pp. 1115–1118. European Language Re- sources Association, Paris, (2004).

      [9] S. Argamon, C. Whitelaw, P. Chase, S. R. Hota, N. Garg, and S. Levitan. Stylistic text classification using functional lexical features: Research articles. J. Am. Soc. Inf. Sci. Technol., 58(6):802–822, Apr. 2007.

      [10] Dr.Seetaiah Kilaru, Hari Kishore K, Sravani T, Anvesh Chowdary L, Balaji T “Review and Analysis of Promising Technologies with Respect to fifth Generation Networksâ€, 2014 First International Conference on Networks & Soft Computing, ISSN:978-1-4799-3486-7/14,pp.270-273,August2014.

      [11] S.V.Manikanthan and T.Padmapriya “Recent Trends In M2m Communications In 4g Networks And Evolution Towards 5gâ€, International Journal of Pure and Applied Mathematics, ISSN NO:1314-3395, Vol-115, Issue -8, Sep 2017.

      [12] T. Padmapriya and V.Saminadan, “Improving Performance of Downlink LTE-Advanced Networks Using Advanced Networks Using Advanced feedback Mechanisms and SINR Modelâ€, International Conference on Emerging Technology (ICET), vol.7, no.1, pp: 93, March 2014.

      [13] Dr.Seetaiah Kilaru, Hari Kishore K, Sravani T, Anvesh Chowdary L, Balaji T “Review and Analysis of Promising Technologies with Respect to fifth Generation Networksâ€, 2014 First International Conference on Networks & Soft Computing, ISSN:978-1-4799-3486-7/14,pp.270-273,August2014.

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  • How to Cite

    Ragupathy, R., & Phaneendra Maguluri, L. (2018). Comparative analysis of machine learning algorithms on social media test. International Journal of Engineering & Technology, 7(2.8), 284-290. https://doi.org/10.14419/ijet.v7i2.8.10425

    Received date: 2018-03-21

    Accepted date: 2018-03-21

    Published date: 2018-03-19