Skip to main content
SpringerLink
Log in

Evaluating Twitter Data to Discover User’s Perception About Social Internet of Things

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Social Internet of Things (SIoT) is a young paradigm that integrates Internet of Things and Social Networks. Social Internet of Things is defined as a social network of intelligent objects. SIoT has led to autonomous decision making and communication between object peers. SIoT has created and opened many research avenues in the recent years and it is vital to understand the impact of SIoT in the real world. In this paper, we have mined twitter to evaluate the user awareness and impact of SIoT among the public. We use R for mining twitter and perform extensive sentiment analysis using supervised and semi supervised algorithms to evaluate the user’s perception about SIoT. Experimental results show that the proposed Fragment Vector model, a semi supervised classification algorithm is better when compared to supervised classification algorithms namely Improved Polarity Classifier (IPC) and SentiWordNet Classifier (SWNC). We also evaluate the combined performance of IPC and SWNC and propose a hybrid classifier (IPC + SWNC). Our analysis was challenged by limited number of tweets with respect to our study. Experimental results using R has produced evidences of its social influences.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer Networks, 54(15), 2787–2805.

    Article  MATH  Google Scholar 

  2. Bian, J., Yoshigoe, K., Hicks, A., Yuan, J., He, Z., Xie, M., et al. (2016). Mining twitter to assess the public perception of the “Internet of Things”. PLoS ONE, 11(7), e0158450.

    Article  Google Scholar 

  3. Kim, J., Yoo, J. B., Lim, H., Qiu, H., Kozareva, Z., & Galstyan, A. (2013). Sentiment prediction using collaborative filtering. In ICWSM.

  4. Balahur, A. (2013). Sentiment analysis in social media texts. In 4th workshop on computational approaches to subjectivity, sentiment and social media analysis (pp. 120–128).

  5. Khan, F. H., Bashir, S., & Qamar, U. (2014). TOM: Twitter opinion mining framework using hybrid classification scheme. Decision Support Systems, 57, 245–257.

    Article  Google Scholar 

  6. Meena Kowshalya, A., & Valarmathi, M. L. (2017). Trust management for reliable decision making among smart objects in the social internet of things. IET Networks, 6(4), 75–80.

    Article  Google Scholar 

  7. Cui, A., Zhang, M., Liu, Y., & Ma, S. (2011). Emotion tokens: Bridging the gap among multilingual twitter sentiment analysis. In Asia information retrieval symposium (pp. 238–249). Springer, Berlin.

  8. Bifet, A., & Frank, E. (2010). Sentiment knowledge discovery in twitter streaming data. In International conference on discovery science (pp. 1–15). Springer, Berlin.

  9. Bifet, A., Holmes, G., & Pfahringer, B. (2011). Moa-tweetreader: Real-time analysis in twitter streaming data. In International conference on discovery science (pp. 46–60). Springer, Berlin.

  10. Tripathy, A., Agrawal, A., & Rath, S. K. (2016). Classification of sentiment reviews using n-gram machine learning approach. Expert Systems with Applications, 57, 117–126.

    Article  Google Scholar 

  11. Catal, C., & Nangir, M. (2017). A sentiment classification model based on multiple classifiers. Applied Soft Computing, 50, 135–141.

    Article  Google Scholar 

  12. Le, Q. V., & Mikolov, T. (2014). Distributed Representations of Sentences and Documents. In ICML (Vol. 14, pp. 1188–1196).

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Government College of Technology, Coimbatore, India

    A. Meena Kowshalya

  2. Department of Electrical and Electronics Engineering, Alagappa Chettiar College of Engineering and Technology, Karaikudi, India

    M. L. Valarmathi

Authors
  1. A. Meena Kowshalya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. L. Valarmathi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. L. Valarmathi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Meena Kowshalya, A., Valarmathi, M.L. Evaluating Twitter Data to Discover User’s Perception About Social Internet of Things. Wireless Pers Commun 101, 649–659 (2018). https://doi.org/10.1007/s11277-018-5709-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-018-5709-2

Keywords

Search

Navigation