Advertisement

Sentiment Analysis on Tweets for Trains Using Machine Learning

  • Sachin KumarEmail author
  • Marina I. Nezhurina
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 942)

Abstract

Sentiment analysis is a popular theme in the natural language processing (NLP) domain. People at present share their stay experience in restaurants, shopping malls, hotels and their travel experience in taxis, buses, trains and airplanes. Online social media provide a platform for the people to share their experiences of stay and travel in the form of text, images and videos. Twitter is one of the popular and well known social media platforms across the world. In this study, we are using tweets data in respect to comfort services in Indian long route superfast trains. This tweet data is used to analyze the hidden sentiments using machine learning techniques such as support vector machines (SVM), Random forest (RF) and back propagation neural networks (BPNN). The results show that BPNN provides high accuracy with more training on the data. The results achieved from SVM and RF was also satisfactory but BPANN won the race with more training on the data.

Keywords

Classification Support vector machines Random forest Twitter Back propagation neural network 

Notes

Acknowledgement

The authors gratefully acknowledge the financial support of the Ministry of Education and Science of the Russian Federation in the framework of Increase Competitiveness Program of NUST « MISiS » (№ К4-2017-052).

References

  1. 1.
    Tiwari, P., Mishra, B.K., Kumar, S., Kumar, V.: Implementation of n-gram methodology for rotten tomatoes review dataset sentiment analysis. Int. J. Knowl. Disc. Bioinf. (IJKDB) 9(1), 30–41 (2017)CrossRefGoogle Scholar
  2. 2.
    Twitter: https://www.twitter.com. Accessed 05 July 2018
  3. 3.
    Microblogs: https://en.wikipedia.org/wiki/Microblogging. Accessed 20 July 2018
  4. 4.
    Elango, V., Narayanan, G.: Sentiment analysis for hotel reviews (2014). http://cs229.stanford.edu/projects2014.html
  5. 5.
    Adeborna, E., Siau, K.: An approach to sentiment analysis – the case of airline quality rating. In: PACIS 2014 Proceedings, Paper 363, Chengdu, 24–28 June (2014)Google Scholar
  6. 6.
    Pouransari, H., Ghili, S.: Deep learning for sentiment analysis of movie reviews (2015). https://cs224d.stanford.edu/reports/PouransariHadi.pdf
  7. 7.
    Doan, T., Kalita, J.: Sentiment analysis of restaurant reviews on yelp with incremental learning. In: 15th IEEE International Conference on Machine Learning and Applications (ICMLA), Anaheim, CA, pp. 697–700 (2016)Google Scholar
  8. 8.
    Ringsquandl, M., Petkovic, D.: Analyzing political sentiment on Twitter. In: Proceedings of AAAI Conference (2013)Google Scholar
  9. 9.
  10. 10.
  11. 11.
    Turney, P.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the Association for Computational Linguistics, pp. 417–424 (2002)Google Scholar
  12. 12.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (2002)Google Scholar
  13. 13.
    Michelle, A., Kondrak, G.: A comparison of sentiment analysis techniques: polarizing movie blogs. In: Advances in Artificial Intelligence, pp. 25–35. Springer, Heidelberg (2008)Google Scholar
  14. 14.
    Liu, B.: Sentiment analysis and subjectivity. In: Handbook of Natural Language Processing, pp. 627–666. CRC Press, Boca Raton (2010)Google Scholar
  15. 15.
    Singh, V.K., Piryani, R., Uddin, A., Waila, P.: Sentiment analysis of movie reviews: a new feature-based heuristic for aspect-level sentiment classification. In: 2013 International Mutli-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4 s), Kottayam, pp. 712–717 (2013)Google Scholar
  16. 16.
    Shi, H., Li, X.: A sentiment analysis model for hotel reviews based on supervised learning. In: 2011 International Conference on Machine Learning and Cybernetics, Guilin, pp. 950–954 (2011)Google Scholar
  17. 17.
    Lacic, E., Kowald, D., Lex, E.: High Enough?: Explaining and predicting traveler satisfaction using airline reviews. In: Proceedings of the 27th ACM Conference on Hypertext and Social Media, July 10–13, Halifax, Nova Scotia, Canada (2016)Google Scholar
  18. 18.
    Zou, X., Yang, J., Zhang, J.: Microblog sentiment analysis using social and topic context. PLoS ONE 13(2), e0191163 (2018)CrossRefGoogle Scholar
  19. 19.
    Bottou, L., Cortes, C., Denker, J.S., Drucker, H., Guyon, I., Jackel, L., LeCun, Y., Muller, U.A., Sackinger, E., Simard, P., Vapnik, V.: Comparison of classifier methods: a case study in handwriting digit recognition. In: International Conference on Pattern Recognition, pp. 77–87 (1994)Google Scholar
  20. 20.
    Knerr, S., Personnaz, L., Dreyfus, G.: Single-layer learning revisited: a stepwise procedure for building and training a neural network. In: Fogelman, J. (ed.) Neurocomputing: Algorithms, Architectures and Applications. Springer (1990)Google Scholar
  21. 21.
    Platt, J.C., Cristianini, N., Shawe-Taylor, J.: Large margin DAGs for multiclass classification. In: Advances in Neural Information Processing Systems, vol. 12, pp. 547–553. MIT Press (2000)Google Scholar
  22. 22.
    Mantyla, M.V., Graziotin, D., Kuutila, M.: The evolution of sentiment analysis-a review of research topics, venues, and top cited papers. Comput. Sci. Rev. 27, 16–32 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.College of IBSNUST MISISMoscowRussia

Personalised recommendations