Skip to main content
SpringerLink
Log in
Book cover

Proceedings of International Conference on ICT for Sustainable Development pp 279–287Cite as

Classifying Nodes in Social Media Space

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 408))

Abstract

Social media provides a platform to interact among people where they share or exchange idea and information. Social network analysis is one of the widest research area used in economics, behavioural, social, political, organizational sciences, etc. Today, maximum information is available online thus a smart system is required to interpret the data. The analysis of information is based on human interaction and the perception of user-generated content. The interpretation fluctuate person-to-person thus automated system is required. In this paper, a methodology is proposed for the classification of node linked with official Panjab University Facebook page.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Luchman, J. N., Bergstrom, J., & Krulikowski, C. (2014). A motives framework of social media website use: A survey of young Americans. Journal of Computers in Human Behavior, 38, 136–141.

    Google Scholar 

  2. Xia, R., Zong, C., & Li, S. (2011). Ensemble of feature sets and classification algorithms for sentiment classification, Journal Elsevier, 1138–1152.

    Google Scholar 

  3. Pang, B., & Lee, L. (2004). A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of the Association for Computational Linguistics (pp. 271–278).

    Google Scholar 

  4. Liu, B. (2012). Sentiment analysis and opinion mining. Morgan & Claypool Publishers.

    Google Scholar 

  5. Hsu, T. S., Liau, C. J., & Wang, D. W. (2014). A logical framework for privacy-preserving social network publication. Journal of Applied Logic, 12, 151–174.

    Article  MathSciNet  MATH  Google Scholar 

  6. Barbier, G., & Liu, H. (2011). Data mining in social media. In C. C. Aggarwal (Ed.), Social network data analytics (pp. 327–352). US: Springer.

    Google Scholar 

  7. Carolina, A., Lima, E. S., & Castro, L. N. (2014) A multi-label, semi-supervised classification approach applied to personality prediction in social media. Journal Neural Networks, 58, 122–130.

    Google Scholar 

  8. Bhadanea, C., Dalalb, H., & Doshi, H. (2015). Sentiment analysis: Measuring opinions. Procedia Computer Science, 45, 808–814.

    Article  Google Scholar 

  9. Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5, 1093–1113.

    Article  Google Scholar 

  10. Bao, S., Xu, S., Zhang, L., Yan, R., Su, Z., Han, D., & Yu, Y. (2012). Mining social emotions from affective text. IEEE Transactions on Knowledge and Data Engineering, 24, 1658–1670.

    Article  Google Scholar 

  11. Hanhoon, K., Joon, Y. S., & Dongil, H. (2012). Senti-lexicon and improved Naïve Bayes algorithms for sentiment analysis of restaurant reviews. Expert Systems with Applications, 39, 6000–6010.

    Google Scholar 

  12. Kaufmann, J. M. (2012). JMaxAlign: A maximum entropy parallel sentence alignment tool. In Proceedings of COLING’12: Demonstration Papers, pp. 277–288.

    Google Scholar 

  13. Aggarwal, C. C., & Zhai C. X. (2012). Mining text data. Springer.

    Google Scholar 

  14. Li Y. M., & Li T. Y. (2013). Deriving market intelligence, from microblogs. Decision Support Systems.

    Google Scholar 

  15. Ruiz, M., & Srinivasan, P. (1999). Hierarchical neural networks for text categorization. In ACM SIGIR Conference.

    Google Scholar 

  16. Tou, N. H., Wei, G., & Kok, L. (1997). Feature selection, perceptron learning, and a usability case study for text ategorization. ACM SIGIR conference.

    Google Scholar 

  17. Rodrigo, M., Francisco, V. J., & Wilso, G. N. (2013). Document-level sentiment classification: an empirical comparison between SVM and ANN. System Applications, 40, 621–33.

    Google Scholar 

  18. Li, Y., & Jain, A. (1998). Classification of text document. Journal of Computer, 41, 537–546.

    Article  MATH  Google Scholar 

  19. Yi, H., Li, W. (2011). Document sentiment classification by exploring description model of topical term. Computer Speech Language, 25, 386–403.

    Google Scholar 

  20. Wilson, T., Wiebe, J., & Hwa, R. (2006). Recognizing strong and weak opinion clauses. Computational Intelligence, 22, 73–99.

    Article  MathSciNet  Google Scholar 

  21. Yulan, H., & Dey, Z. (2011). Self-training from labeled features for sentiment analysis. Information Processing and Management, 47, 606–616.

    Article  Google Scholar 

  22. Fu, X., Liu, G., Guo, Y., & Wang, Z. (2013). Multiaspect sentiment analysis for Chinese online social reviews based on topic modeling and how net lexicon. Knowledge-Based Systems, 37, 186–195.

    Google Scholar 

  23. Montejo-Raez, A., Martınez-Camara, E., Martın-Valdivia, M. T., & Urena-Lopez, L. A. (2012). Random walk weighting over sentiwordnet for sentiment polarity detection on twitter. In Proceedings of the 3rd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (pp. 3–10).

    Google Scholar 

  24. Lin, C., & He, Y. (2009). Joint sentiment/topic model for sentiment analysis. In Proceedings of CIKM (pp. 375–384).

    Google Scholar 

  25. Khan, F. H., Bashir, S., & Qamar, U. (2009). TOM: Twitter opinion mining framework using hybrid classification scheme. Journal of Decision Support Systems, 57, 245–257.

    Article  Google Scholar 

  26. Moraes, R., Valiati, J. F., Wilson, P., & Gaviao, N. (2013). Document-level sentiment classification: An empirical comparison between SVM and ANN. Expert Systems with Applications, 40, 621–633.

    Article  Google Scholar 

  27. Carolina, A., Lima, E. S., & Castro, L. N. (2014). A multi-label, semi-supervised classification approach applied to personality prediction in social media. Neural Networks, 58, 122–130.

    Article  Google Scholar 

  28. Deng, Z. H., Luo, K. H., & Yu, H. L. (2014). A study of supervised term weighting scheme for sentiment analysis. Expert Systems with Applications, 41, 3506–3513.

    Article  Google Scholar 

  29. Dam, J. W., & Velden, M. (2015). Online profiling and clustering of Facebook users. Decision Support Systems, 70, 60–72.

    Article  Google Scholar 

  30. Alm, C. O., Roth, D., & Sproat, R.(2005) Emotions from text: machine learning for text-based emotion prediction. In Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP) (pp. 579–586).

    Google Scholar 

  31. Ho, T., Hull, J., & Srihari, S. (1994). Decision combination in multiple classifier systems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16, 66–75.

    Article  Google Scholar 

  32. Kittler, J. (1998). Combining classifiers: a theoretical framework. Pattern Analysis and Applications, 1, 18–27.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University Institute of Engineering and Technology, Panjab University, Chandigarh, India

    Kirti Thakur & Harish Kumar

Authors
  1. Kirti Thakur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Harish Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kirti Thakur .

Editor information

Editors and Affiliations

  1. Dept of Comp Sci Engg, Anil Neerukonda Ins Tech & Sci, Visakhapatnam, Andhra Pradesh, India

    Suresh Chandra Satapathy

  2. Sabarkantha, Sabar Institute of Technology, Sabarkantha, Gujarat, India

    Amit Joshi

  3. Studies and Management, Narsinhbhai Institute of Computer, Kadi, Gujarat, India

    Nilesh Modi

  4. Computer Studies and Management, Narsinhbhai Institute of, Kadi, Gujarat, India

    Nisarg Pathak

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer Science+Business Media Singapore

About this paper

Cite this paper

Thakur, K., Kumar, H. (2016). Classifying Nodes in Social Media Space. In: Satapathy, S., Joshi, A., Modi, N., Pathak, N. (eds) Proceedings of International Conference on ICT for Sustainable Development. Advances in Intelligent Systems and Computing, vol 408. Springer, Singapore. https://doi.org/10.1007/978-981-10-0129-1_30

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-0129-1_30

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0127-7

  • Online ISBN: 978-981-10-0129-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation