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Tweet Analysis Based on Distinct Opinion of Social Media Users’

  • S. GeethaEmail author
  • Kaliappan Vishnu Kumar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 750)

Abstract

The state of mind gets expressed via Emojis’ and Text Messages for the huge population. Micro-blogging and social networking sites emerged as a popular communication channels among the Internet users. Supervised text classifiers are used for sentimental analysis in both general and specific emotions detection with more accuracy. The main objective is to include intensity for predicting the different texts formats from twitter, by considering a text context associated with the emoticons and punctuations. The novel Future Prediction Architecture Based On Efficient Classification (FPAEC) is designed with various classification algorithms such as, Fisher’s Linear Discriminant Classifier (FLDC), Support Vector Machine (SVM), Naïve Bayes Classifier (NBC), and Artificial Neural Network (ANN) Algorithm along with the BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) clustering algorithm. The preliminary stage is to analyze the distinct classification algorithm’s efficiency, during the prediction process and then the classified data will be clustered to extract the required information from the trained dataset using BIRCH method, for predicting the future. Finally, the performance of text analysis can get improved by using efficient classification algorithm.

Keywords

Text classifiers Emoticons Twitter Social networking 

References

  1. 1.
  2. 2.
    Wakade, S., Shekar, C., Liszka, K.J., Chan, C.-C.: Text mining for sentiment analysis of twitter data. The University of Akron (2012)Google Scholar
  3. 3.
    Younis, E.M.G.: Sentiment analysis and text mining for social media microblogs using open source tools: an empirical study. IEEE Access (2015)Google Scholar
  4. 4.
    Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., Qin, B.: Learning sentiment-specific word embedding for twitter sentiment classification. IEEE Access (2014)Google Scholar
  5. 5.
    da Silva, N.F.F., Hruschka, E.R., Hruschka, E.R.: Tweet sentiment analysis with classifier ensembles. DECSUP-12515. Federal University, Brazil (2014)Google Scholar
  6. 6.
    Omar, M.S., Njeru, A., Paracha, S., Wannous, M., Yi, S.: Mining tweets for education reforms (2017). ISBN 978-1-5090-4897-7Google Scholar
  7. 7.
    Wehrmann, J., Becker, W., Cagnini, H.E.L., Barros, R.C.: A character-based convolutional neural network for language-agnostic twitter sentiment analysis. IEEE Access (2017)Google Scholar
  8. 8.
    Jiang, D., Luo, X., Xuan, J., Xu, Z.: Sentiment computing for the news event based on the social media big data. IEEE Access (2016)Google Scholar
  9. 9.
    Jianqiang, Z., Xiaolin, G.: Comparison research on text pre-processing methods on twitter sentiment analysis. IEEE Access (2017)Google Scholar
  10. 10.
    Williams, G., Mahmoud, A.: Analyzing, classifying, and interpreting emotions in software users’ tweets. IEEE Access (2017)Google Scholar
  11. 11.
    Batool, R., Khattak, A.M., Maqbool, J., Lee, S.: Precise tweet classification and sentiment analysis. IEEE Access (2013)Google Scholar
  12. 12.
    Afroz, N., Asad, M.-U., Dey, L.: An intelligent framework for text-to-emotion analyzer. IEEE Access (2015)Google Scholar
  13. 13.
    Sowmiya, J.S., Chandrakala, S.: Joint sentiment/topic extraction from text. In: IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT) (2014)Google Scholar
  14. 14.
    Akaichi, J.: Social networks’ facebook’ statutes updates mining for sentiment classification. Computer Science Department, IEEE (2013). doi: 10.1109Google Scholar
  15. 15.
    Chaugule, A., Gaikwad, S.V.: Text mining methods and techniques. Int. J. Comput. Appl. 85(17) (2014)Google Scholar
  16. 16.
    De Choudhury, M., Ahsan, U.: Towards using visual attributes to infer image sentiment of social events. IEEE Access (2017)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.CSE DepartmentKPR Institute of Engineering and TechnologyCoimbatoreIndia

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