Real Time Opinion Mining and Analysis of Twitter Data

  • Authors

    • K Senthil Kumar
    • Mohammad Musab Trumboo
    • Vaibhav .
    • Satyajai Ahlawat
    2018-07-20
    https://doi.org/10.14419/ijet.v7i3.12.16104
  • Sentimental Analysis, TextBlob, Naive Bayes Classifier, NLP, Tweepy, Twitter API.

  • This era, in which we currently stand, is an era of public opinion and mass information. People from all around the globe are joined together through various information junctions to create a global community, where one thing from the far east reaches to the people of the far west within seconds. Nothing is hidden, everything and anything can be scrutinized to its core and through these global criticisms and mass discussions of gigantic magnitude, we have reached to the pinnacle of correct decisions and better choices. These pseudo social groups and data junctions have bombarded our society so much that they now hold the forelock of our opinions and sentiments, ergo, we reach out to these groups to achieve a better outcome. But, all this enormous data and all these opinions cannot be researched by a single person, hence, comes the need of sentiment analysis. In this paper we’ll try to accomplish this by creating a system that will enable us to fetch tweets from twitter and use those tweets against a lexical database which will create a training set and then compare it with the pre-fetched tweets. Through this we will be able to assign a polarity to all the tweets by means of which we can address them as negative, positive or neutral and this is the very foundation of sentiment analysis, so subtle yet so magnificent.

     

     

  • References

    1. [1] Selvan, L.G.S. and Moh, T.S. “A Framework for Fast-Feedback Opinion Mining on Twitter Data Streams.†Published in: Collaboration Technologies and Systems (CTS), 2015 International Conference on 1-5 June 2015.

      [2] Rana, S. and Singh, A. “Comparative Analysis of Sentiment Orientation Using SVM and Naïve Bayes Techniques.†2nd International Conference on Next Generation Computing Technologies (NGCT-2016) Dehradun, India 14-16 October 2016.

      [3] Trupthi, M., Pabboju, S. and Narasimha, G. “Sentiment Analysis On Twitter Using Streaming Api.†Published in Advance Computing Conference (IACC), 2017 IEEE 7th International 5-7 Jan. 2017.

      [4] Soni, A.K. “Multi-Lingual Sentiment Analysis of twitter data by using classification algorithms.†Published in Electrical, Computer and Communication Technologies (ICECCT), 2017 Second International Conference on 22-24 Feb. 2017.

      [5] Phand, S.A. and Phand, J.A. “Twitter Sentiment Classification using Stanford NLP†Published in Intelligent Systems and Information Management (ICISIM), 2017 1st International Conference on 5-6 Oct. 2017.

      [6] Mehra, R., Bedi, M.K., Singh, G., Arora, R., Bala, T. and Saxena, S. “Sentimental Analysis Using Fuzzy and Naive Bayes.†Proceedings of the IEEE 2017 International Conference on Computing Methodologies and Communication (ICCMC).

      [7] Goel, A., Gautam, J. and Kumar, S. “Real Time Sentiment Analysis of Tweets Using Naive Bayes.†2nd International Conference on Next Generation Computing Technologies (NGCT-2016) Dehradun, India 14-16 October 2016.

      [8] Batrinca, B. and Treleaven, P.C. “Social media analytics: a survey of techniques, tools and platforms.†AI and Society. Volume 30. Issue 1(2014). pp 89-116.

      [9] Fang, X. and Zhan, J. “Sentiment Analysis Using Product Review Data.†Journal Of Big Data (2015) 2:5

      [10] Nielsen, F.A. “A new ANEW: Evaluation of a word list for sentiment analysis in microblogs.†Proceedings of the ESWC2011 Workshop on 'Making Sense of Microposts': Big things come in small packages (2011) 93-98.

      [11] Kharde, V.A. and Sonawane, S. “Sentiment Analysis of Twitter Data: A Survey of Techniques.†International Journal of Computer Applications 139(11): 5-15, April 2016.

      [12] Hutto, C.J. and Gilbert, E. “VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text.†Proceedings of the Eighth International Association for the Advancement of Artificial Intelligence (AAAI) Conference on Weblogs and Social Media.

      [13] Salathé, M. and Khandelwal, S. “Assessing Vaccination Sentiments with Online Social Media: Implications for Infectious Disease Dynamics and Control.†PLoS Computational Biology 7(10): e1002199

      [14] Jain, V. “Prediction of Movie Success using Sentiment Analysis of Tweets.†The International Journal of Soft Computing and Software Engineering [JSCSE], Vol. 3, No. 3, Special Issue: The Proceeding of International Conference on Soft Computing and Software Engineering 2013 [SCSE’13], San Francisco State University, CA, U.S.A., March 2013.

      [15] Gunther, T and Furrer, L. “GU-MLT-LT: Sentiment Analysis of Short Messages using Linguistic Features and Stochastic Gradient Descent.†Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Seventh International Workshop on Semantic Evaluation (SemEval 2013), pages 328–332, Atlanta, Georgia, June 14-15, 2013.

      [16] Palanisamy, P., Yadav, V. and Elchuri, H. “Serendio: Simple and Practical lexicon based approach to Sentiment Analysis.†Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Seventh International Workshop on Semantic Evaluation (SemEval 2013), pages 543–548, Atlanta, Georgia, June 14-15, 2013.

      [17] Filho, P.P.B. and Pardo, T.A.S. “NILC_USP: A Hybrid System for Sentiment Analysis in Twitter Messages.†Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Seventh International Workshop on Semantic Evaluation (SemEval 2013), pages 568–572, Atlanta, Georgia, June 14-15, 2013.

      [18] Rosenthal, S., Farra, N. and Nakov, P. “SemEval-2017 Task 4: Sentiment Analysis in Twitter.†Proceedings of the 11th International Workshop on Semantic Evaluations (SemEval-2017), pages 502–518, Vancouver, Canada, August 3 - 4, 2017.

      [19] Coppersmith, G., Dredze, M. and Harman, C. “Quantifying Mental Health Signals in Twitter.†Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, pages 51–60, Baltimore, Maryland USA, June 27, 2014.

      [20] Anuradha, G. and Varma, D.J. “Fuzzy Based Summarization of Product Reviews for Better Analysis†Indian Journal of Science and Technology, Vol 9(31), August 2016.

  • Downloads

  • How to Cite

    Senthil Kumar, K., Musab Trumboo, M., ., V., & Ahlawat, S. (2018). Real Time Opinion Mining and Analysis of Twitter Data. International Journal of Engineering & Technology, 7(3.12), 351-353. https://doi.org/10.14419/ijet.v7i3.12.16104