Skip to main content
SpringerLink
Log in

Hybrid N-gram model using Naïve Bayes for classification of political sentiments on Twitter

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Twitter, an online micro-blogging and social networking service, provides registered users the ability to write in 140 characters anything they wish and hence providing them the opportunity to express their opinions and sentiments on events taking place. Politically sentimental tweets are top-trending tweets; whenever election is near, users tweet about their favorite candidates or political parties and at times give their reasons for that. In this study, we hybridize two n-gram [two n-gram models used in this study are unigram and n-gram. Therefore, in this study, where unigram is mentioned that refers to a least-order n-gram (unigram) and where n-gram is mentioned that refers to the highest-order (full sentence or tweet level) n-gram] models and applied Laplace smoothing to Naïve Bayesian classifier and Katz back-off on the model. This was done in order to smoothen and address the limitation of accuracy in terms of precision and recall of n-gram models caused by the ‘zero count problem.’ Result from our baseline model shows an increase of 6.05% in average F-Harmonic accuracy in comparison with the n-gram model and 1.75% increase in comparison with the semantic-topic model proposed from a previous study on the same dataset, i.e., Obama–McCain dataset.

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
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Mitch W (2008) Obama election ushering in first internet presidency. InformationWeek Government. https://www.informationweek.com/government/obama-election-ushering-in-first-internet-presidency/d/d-id/1073714?. Accessed 26 June 2014

  2. Kharde VA, Sonawane SS (2016) Sentiment analysis of Twitter data: a survey of techniques. Int J Comput Appl 139(11):5–15

    Google Scholar 

  3. Cambria E (2016) Affective computing and sentiment analysis. IEEE Intell Syst 16(March/April):102–107

    Article  Google Scholar 

  4. Perikos I, Hatzilygeroudis I (2016) A classifier ensemble approach to detect emotions polarity in social media. In: Proceedings of the 12th international conference on web information systems and technologies (WEBIST 2016), vol 1, pp 363–370

  5. Shaheen S, El-Hajj W, Hajj H, Elbassuoni S (2014) Emotion recognition from text based on automatically generated rules. In: IEEE international conference on data mining workshop, pp 383–392. http://doi.org/10.1109/ICDMW.2014.80

  6. Fersini E, Messina E, Pozzi FA (2014) Sentiment analysis: Bayesian ensemble learning. Decis Support Syst 68(2014):26–38. https://doi.org/10.1016/j.dss.2014.10.004

    Article  Google Scholar 

  7. Bravo-marquez F, Frank E, Pfahringer B (2016) From opinion lexicons to sentiment classification of Tweets and vice versa: a transfer learning approach. In: The 2016 IEEE/WIC/ACM international conference on web intelligence at Omaha, Nebraska

  8. Boroş T, Ştefănescu D, Ion R (2013) Handling two difficult challenges for text-to-speech synthesis systems: out-of-vocabulary words and prosody: a case study in Romanian. In: Neustein A, Markowitz J (eds) Where humans meet machines. Springer, New York

    Google Scholar 

  9. Zhu L, Smith ML, Lerman K, Kozareva Z (2013) The role of social media in the discussion of controversial topics. Accessed on 10 Jan 2014

  10. Mukund D, Avik S (2010) BI and sentiment analysis. Bus Intell J 15(2):41–43

    Google Scholar 

  11. Cambria E, Schuller B, Xia Y, Havasi C (2013) New avenues in opinion mining and sentiment analysis. IEEE Intell Syst 28(2):15–21

    Article  Google Scholar 

  12. Jagtap VS, Pawar K (2013) Analysis of different approaches to sentence-level sentiment classification. Int J Sci Eng Technol 2(3):164–170

    Google Scholar 

  13. Saif H, He Y, Alani H (2012) Semantic sentiment analysis of Twitter. In: 11th international semantic Web conference, Boston, MA, USA, November 11–15, 2012, proceedings, part I, pp 508–524

  14. Tobias G, Johansson R (2013) Sentiment analysis of microblogs. Dissertation, University of Gothenberg

  15. Wang S, Manning CD (1998) Baselines and bigrams: simple, good sentiment and topic classification, vol 94305(1)

  16. Tan S et al (2009) Adapting Naive Bayes to domain adaptation for sentiment analysis. In: Proceedings of the 31th European conference on IR research on advances in information retrieval, pp 337–349

    Google Scholar 

  17. Rennie JDM, Shih L, Teevan J, Karger DR (2003) Tackling the poor assumptions of naive bayes text classifiers. In: Proceedings of the twentieth international conference on machine learning (ICML-2003). AAAI Press, Washington, DC, pp 616–623

    Google Scholar 

  18. Rish I (2001) An empirical study of the Naive Bayes classifier. IBM Research Report. IBM Research Division, pp 41–46

  19. Araque O, Corcuera-Platas I, Sánchez-Rada JF, Iglesias CA (2017) Enhancing deep learning sentiment analysis with ensemble techniques in social applications. Expert Syst Appl 77:236–246. https://doi.org/10.1016/j.eswa.2017.02.002

    Article  Google Scholar 

  20. Mensikova A, Mattmann CA (2018) Ensemble sentiment analysis to identify human trafficking in web data. In: Proceedings of ACM workshop on graph techniques for adversarial activity analytics (GTA32018), pp 0–5

  21. Hande K (2017) Sentiment analysis using ensemble classifier. Int J Innov Res Comput Commun Eng 5(9):15125–15130. https://doi.org/10.15680/IJIRCCE.2017

    Article  Google Scholar 

  22. Ekman P (1999) Basic emotions. In: Handbook of cognition and emotion, pp 45–60

    Chapter  Google Scholar 

  23. da Silva NF, Hruschka ER, Hruschka ER Jr (2014) Tweet sentiment analysis with classifier ensembles. Decis Support Syst 66:170–179. https://doi.org/10.1016/j.dss.2014.07.003

    Article  Google Scholar 

  24. Narayanan V, Arora I, Bhatia A (2013) Fast and accurate sentiment classification using an enhanced Naive Bayes model. In: Proceedings of 14th international conference, IDEAL 2013, Hefei, China, October 20–23, 2013, pp 1–8

    Chapter  Google Scholar 

  25. Jurafsky D, Martin JH (2007) Speech and language processing. In: An introduction to speech recognition, computational linguistics and natural language processing, p 42

  26. Xiang B (2014) Improving Twitter sentiment analysis with topic-based mixture modeling and semi-supervised training. In: Proceedings of the 52nd annual meeting of the association for computational linguistics, pp 434–439

  27. Jurafsky D, Martin JH (2008) Speech and language processing. N-grams, 2nd edn. Prentice Hall, Englewood Cliffs, pp 83–121

    Google Scholar 

  28. Peng F (2003) Augmenting Naive Bayes classifiers with statistical language models. University of Massachusetts—Amherst, Computer Science Department, Faculty Publication Series 91. https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1090&context=cs_faculty_pubs. Accessed 26 June 2014

  29. Bo P, Lillian L (2005) Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. In: Proceedings of the ACL

  30. Riaz M (2018) Fuzzy parameterized fuzzy soft compact spaces with decision-making. J Math 50(2):131–145

    MathSciNet  Google Scholar 

  31. Riaz M, Hashmi MR (2018) Fixed points of fuzzy neutrosophic soft mapping with decision-making. Fixed Point Theory Appl 2018(1):7. https://doi.org/10.1186/s13663-018-0632-5

    Article  Google Scholar 

  32. Sa M, Riaz M, Hashmi MR (2016) Fuzzy parameterized fuzzy soft topology with applications. Ann Fuzzy Math Inf 13(5):593–613

    MathSciNet  MATH  Google Scholar 

  33. Saif H, Fernandez M, He Y, Alani H (2013) Evaluation datasets for Twitter sentiment analysis: a survey and a new dataset, the STS-Gold. In: 1st international workshop on emotion and sentiment in social and expressive media: approaches and perspectives from AI (ESSEM 2013)

Download references

Author information

Authors and Affiliations

  1. Universiti Kebangsaan Malaysia, Bangi, Malaysia

    Jamilu Awwalu, Azuraliza Abu Bakar & Mohd Ridzwan Yaakub

Authors
  1. Jamilu Awwalu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Azuraliza Abu Bakar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohd Ridzwan Yaakub
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jamilu Awwalu.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interests with anybody or any institution regarding the publication of this paper.

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

Awwalu, J., Bakar, A.A. & Yaakub, M.R. Hybrid N-gram model using Naïve Bayes for classification of political sentiments on Twitter. Neural Comput & Applic 31, 9207–9220 (2019). https://doi.org/10.1007/s00521-019-04248-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-019-04248-z

Keywords

Search

Navigation