Open Access Open Access  Restricted Access Subscription Access

Twitter Opinion Analysis about 5G Technology


Affiliations
1 Ph.D. Research Scholar, Department of Computer Science, Punjabi University, Patiala, India
 

Since thousands of users freely express their opinions on Twitter every day, it has become a rich source for sentiment analysis and opinion mining data. In this investigation, we look at the sentiment of shared articles with the hashtag "#5G" and categories it as positive, negative, or neutral. We used statistical sentiment analysis tool to create a classification model that had an accuracy and recall of 83.69%. The findings indicate that it is possible to recognize key public opinion factors in the acceptance or rejection of 5G technology, which is valuable information for technology companies.

Keywords

Twitter, Sentiment Analysis, 5G Technology.
User
Notifications
Font Size

  • Agarwal, A., Agarwal, K., Agarwal, S., & Misra, G. (2019). Evolution of Mobile Communication Technology towards 5G Networks and Challenges. American Journal of Electrical and Electronic Engineering, 7(2), 34–37.
  • Bermingham, A., & Smeaton, A. F. (2011). On Using Twitter to Monitor Political Sentiment and Predict Election Results. Psychology, 2–10.
  • Cavazos-Rehg, P. A., Zewdie, K., Krauss, M. J., & Sowles, S. J. (2018). “No High Like a Brownie High”: A Content Analysis of Edible Marijuana Tweets. American Journal of Health Promotion, 32(4), 880–886.
  • Chamlertwat, W., Bhattarakosol, P., Rungkasiri, T., & Haruechaiyasak, C. (2012). Discovering consumer insight from twitter via sentiment analysis. Journal of Universal Computer Science, 18(8), 973–992.
  • Filho, R. M., Almeida, J. M., & Pappa, G. L. (2015). Twitter population sample bias and its impact on predictive outcomes: A case study on elections. Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015, April 2017, 1254–1261.
  • Global Digital Report: Digital in 2019. (n.d.). Retrieved August 30, 2019, from https://wearesocial.com/globaldigital-%0Areport-2019%0A
  • Go, A., Bhayani, R., & Huang, L. (2009). Twitter Sentiment Classification using Distant Supervision. Processing, 1–6.
  • Gohil, A., Modi, H., & Patel, S. K. (2013). 5G technology of mobile communication: A survey. 2013 International Conference on Intelligent Systems and Signal Processing, ISSP 2013, February 2015, 288–292.
  • Hoffman, M. D., Blei, D. M., & Bach, F. (2010). Online Learning for latent Dirichlet allocation (Supplementary Material). Nature, 1–9. http://papers.nips.cc/paper/3902-online-learningforlatent-dirichlet-allocation.pdf
  • Holzinger, A. (2017). Introduction to MAchine Learning & Knowledge Extraction (MAKE). Machine Learning and Knowledge Extraction, 1(1), 1–20.
  • Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1–167.
  • Loyola-Gonzalez, O., Monroy, R., Rodriguez, J., Lopez-Cuevas, A., & Mata-Sanchez, J. I. (2019). Contrast Pattern-Based Classification for Bot Detection on Twitter. IEEE Access, 7, 45800–45817.
  • Mane, S. B., Sawant, Y., Kazi, S., & Shinde, V. (2014). Real Time Sentiment Analysis of Twitter Data Using Hadoop. International Journal of Computer Science and Information Technologies, 5(3), 3098–3100.
  • Martin-Domingo, L., Martín, J. C., & Mandsberg, G. (2019). Social media as a resource for sentiment analysis of Airport Service Quality (ASQ). Journal of Air Transport Management, 78(January), 106–115.
  • Neethu, M. S., & Rajasree, R. (2013). Sentiment analysis in twitter using machine learning techniques. 2013 4th International Conference on Computing, Communications and Networking Technologies, ICCCNT 2013.
  • Reyes-Menendez, A., Saura, J. R., & Alvarez-Alonso, C. (2018). Understanding #worldenvironmentday user opinions in twitter: A topic-based sentiment analysis approach. International Journal of Environmental Research and Public Health, 15(11).
  • Samuels, A., & McGonical, J. (2020). Sentiment Analysis on Social Media Content. ArXiv.
  • Saura, J. R., Palos-Sanchez, P., & Martin, M. A. R. (2018). Attitudes expressed in online comments about environmental factors in the tourism sector: An exploratory study. International Journal of Environmental Research and Public Health, 15(3).
  • Small, T. A. (2011). What the hashtag?: A content analysis of Canadian politics on Twitter. Information Communication and Society, 14(6), 872–895.
  • Wang, H., Can, D., Kazemzadeh, A., Bar, F., & Narayanan, S. (2012). A System for Real-time Twitter Sentiment Analysis of 2012 U.S. Presidential Election Cycle. Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, July, 115–120.
  • Wang, W., & Wu, J. (2011). Emotion Recognition Based on CSO&SVM in E- Learning. 2011 Seventh International Conference on Natural Computation, 1, 566–570.
  • Yu, Y., Duan, W., & Cao, Q. (2013). The impact of social and conventional media on firm equity value: A sentiment analysis approach. Decision Support Systems, 55(4), 919–926.

Abstract Views: 284

PDF Views: 1




  • Twitter Opinion Analysis about 5G Technology

Abstract Views: 284  |  PDF Views: 1

Authors

Mukhtiar Singh
Ph.D. Research Scholar, Department of Computer Science, Punjabi University, Patiala, India

Abstract


Since thousands of users freely express their opinions on Twitter every day, it has become a rich source for sentiment analysis and opinion mining data. In this investigation, we look at the sentiment of shared articles with the hashtag "#5G" and categories it as positive, negative, or neutral. We used statistical sentiment analysis tool to create a classification model that had an accuracy and recall of 83.69%. The findings indicate that it is possible to recognize key public opinion factors in the acceptance or rejection of 5G technology, which is valuable information for technology companies.

Keywords


Twitter, Sentiment Analysis, 5G Technology.

References