Sentiment Analysis of Movie Review using Machine Learning Techniques

  • Abstract
  • Keywords
  • References
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  • Abstract

    Today's online world was fully filled up with blogs, views, comments, posts through various websites and social-surfs. People were habituated with posting every incident into blogs, messed with comments like text and emotions, which are a mixed bag of sad, happy, worry, cry etc. Analysing such data was called as Sentimental Analysis. To analysis, these unordered data we use new emerged technology algorithms. Machine learning a transpire technology which is engaged with almost all the fields, where its algorithms are more powerful that give with better faultless results. In this paper, we are analyzing tweets based on movie reviews using the Multinomial Logistic Regression, Naïve Bayes, and SVM algorithms to compare score value to show the best text analysis algorithm.


  • Keywords

    Sentiment Analysis, Opinion Mining, Twitter Analysis, Machine Learning, Natural Language Processing.

  • References

      [1] B Pang, L Lee- “Opinion mining and sentiment analysis” Foundations and Trends in Information Retrieval Vol. 2, No 1-2 (2008) 1–135

      [2] A Pak, P Paroubek- LREc, “Twitter as a corpus for sentiment analysis and opinion mining” International Conference on Language Resources and Evaluation, LREC 2010, 17-23 May 2010, Valletta, Malta

      [3] Khimar, J., Kinikar, M. (2013). "Machine Learning Algorithms for Opinion Mining and Sentiment Classification". International Journal of Scientific and Research Publications, 3(6),1-6, ISSN 2250-3153, Volume 3, Issue 6, June 2013.

      [4] Jayashri Khaimar* Mayura Kinikar, “Machine Learning Algorithm for Opinion Mining and Sentiment Classification”, International Journal of Scientific and Research Publications, 1 ISSN 2250-3153, Volume 3, Issue 6, June 2013

      [5] Ruchi Mehral Mandeep Kaur Bedi2, Gagandeep Singh3, Raman Anaroa4, Tannu Bala5, Sunny Sazena6 Webtunix Solutions Pvt, Ltd. Mohali, “Sentimental Analysis using Fuzzy and Naïve Bayes” International Journal of Scientific and Research Publications, ISSN 2250-3153, Volume 3, Issue 6, June 2013

      [6] Bingwei Liu∗, Erik Blasch†, Yu Chen‡, Dan Shen∗ and Genshe Chen, “Scalable Sentiment Classification for Big Data Analysis Using Na¨ıve Bayes Classifier” Big Data, IEEE International Conference,2103 Electronic ISBN: 978-1-4799-1293-3, December 2013

      [7] Shweta Rana, Archana Singh “Comparative analysis of sentiment orientation using SVM and Naïve Bayes techniques”, Next Generation Computing Technologies(NGCT), Electronic ISBN: 978-1-5090-3257-0, 2016.

      [8] Ankur Goel, Jyoti Gautam, Sitesh Kumar, “Real-time sentiment analysis of tweets using Naïve Bayes”, Next Generation Computing Technologies(NGCT), Electronic ISBN: 978-1-5090-3257-0, 2016.

      [9] Human Parveen Shikha Pandey “Sentiment analysis on twitter Data-set using Naïve Bayes algorithm”, Applied and Theoretical Computing and Communication Technology (iCATccT), Electronic ISBN: 978-1-5090-2399-8, 2016.

      [10] Vryniotis Vasils and Vasilis Vryniotis” Machine Learning Tutorial: The Multinomial Logistic Regression (Softmax Regression) May 2017.

      [11] Zainuddin, Nurulhuda and A. Selamat,” Sentiment Analysis Using Support Vector Machine”, International Conferences on Computer, 2014.

      [12] T Gunasekhar KT Rao, MT Basu “Understanding insider attack problem and scope in cloud” Circuit, Power and Computing Technologies(ICCPCT), 2015

      [13] T Gunasekhar, KT Rao, “EBCM: Single encryption, multiple decryptions”, International Journal of Applied Engineering Research 2014.

      [14] KT Rao, PS Kiran, LSS Reddy “ High-Level Architecture to provide cloud services Using Green Data Center”, Advances in Wireless and Mobile Communications (AWMC), 2014.

      [15] KT Rao, PS Kiran, DLSS Reddy, VK Reddy, BT Rao, “Genetic Algorithm for Energy Placement Of Virtual Machines in Cloud Environment”. proc IEEE International Conference on Future Information Technology, 2012.

      [16] Ramadhan WP, strip Novianty S.T, Casi Setianing S.T., M.T “Sentiment Analysis Using Multinomial Logistic Regression”, International Conference on control, Electronics, Renewable Energy and communication(ICCEREC), 2017

      [17] Vishal A. Kharde, S.S Sonawane “ Sentiment Analysis od Twitter Data: A Survey of techniques”, International Journal of Computer Applications, Volume 139-no.11April 2016.

      [18] KISHORE, P.V.V., KISHORE, S.R.C. and PRASAD, M.V.D., 2013. Conglomeration of hand shapes and texture information for recognizing gestures of indian sign language using feed forward neural networks. International Journal of Engineering and Technology, 5(5), pp. 3742-3756.

      [19] RAMKIRAN, D.S., MADHAV, B.T.P., PRASANTH, A.M., HARSHA, N.S., VARDHAN, V., AVINASH, K., CHAITANYA, M.N. and NAGASAI, U.S., 2015. Novel compact asymmetrical fractal aperture Notch band antenna. Leonardo Electronic Journal of Practices and Technologies, 14(27), pp. 1-12.

      [20] KARTHIK, G.V.S., FATHIMA, S.Y., RAHMAN, M.Z.U., AHAMED, S.R. and LAY-EKUAKILLE, A., 2013. Efficient signal conditioning techniques for brain activity in remote health monitoring network. IEEE Sensors Journal, 13(9), pp. 3273-3283.

      [21] KISHORE, P.V.V., PRASAD, M.V.D., PRASAD, C.R. and RAHUL, R., 2015. 4-Camera model for sign language recognition using elliptical fourier descriptors and ANN, International Conference on Signal Processing and Communication Engineering Systems - Proceedings of SPACES 2015, in Association with IEEE 2015, pp. 34-38.




Article ID: 10921
DOI: 10.14419/ijet.v7i2.7.10921

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