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International Conference on Intelligent Technologies and Applications

INTAP 2018: Intelligent Technologies and Applications pp 86–97Cite as

Sentiment Analysis of Student’s Facebook Posts

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 932))

Abstract

Students are the major part of the colleges/universities and a beneficial of the institutions. Students reviews and opinions are important to improve the institutional problem, matters, and issues. The success of any college/university is to increase the students’ satisfaction level and it’s good for increasing the ranking of the institution. Its paper target the students’ sentiments post on Facebook colleges/university groups to express their behaviors, opinions, and views related curriculum and extra curriculum activities. Developing an automated system, use the students’ post of the Facebook group to implement the Novel approach. This paper uses the dataset that is based on the issues of the National College of Business Administration & Economics. This research work provides the automated system to detect the students’ post of related issues is positive, negative and neutral. Developing tool provides the best outcomes to improve the related issues, matters and institution’s policies.

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References

  1. Zamani, N.A.M., Abidin, S.Z.: Sentiment analysis: determining people’s emotions in Facebook. In: Proceedings of the 13th International Conference on Applied Computer and Applied Computational Science, pp. 111–116 (2014)

    Google Scholar 

  2. Akaichi, J.: Social networks’ Facebook’ statutes updates mining for sentiment classification. In: International Conference on Social Computing (SocialCom), pp. 886–891. IEEE, Alexandria (2013)

    Google Scholar 

  3. Troussas, C., Virvou, M.: Sentiment analysis of Facebook statuses using Naive Bayes classifier for language learning. In: Fourth International Conference on Information, Intelligence, Systems and Applications (IISA), pp. 1–6. IEEE (2013)

    Google Scholar 

  4. Mesnil, G., Mikolov, T.: Ensemble of generative and discriminative techniques for sentiment analysis of movie reviews, arXiv preprint arXiv:1412.5335, pp. 1–5 (2014)

  5. Bosco, C.: Developing corpora for sentiment analysis: the case of irony and senti-tut. IEEE Intell. Syst. 28(2), 55–63 (2013)

    Google Scholar 

  6. West, R., Paskov, H.S.: Exploiting social network structure for person-to-person sentiment analysis. arXiv preprint arXiv:1409.2450 (2014)

  7. Ortigosa, A.: Sentiment analysis in Facebook and its application to e-learning. Comput. Hum. Behav. 31, 527–541 (2014)

    Google Scholar 

  8. Medhat, W.: Sentiment analysis algorithms and applications: a survey. Ain Shams Eng. J. 5(4), 1093–1113 (2014)

    Google Scholar 

  9. De Boom, C., Van Canneyt, S.: Learning semantic similarity for very short texts. In: IEEE International Conference on Data Mining Workshop (ICDMW), pp. 1229–1234. IEEE (2015)

    Google Scholar 

  10. Sahayak, V.: Sentiment analysis on twitter data. Int. J. Innov. Res. Adv. Eng. (IJIRAE) 2(1), 178–183 (2015)

    Google Scholar 

  11. Rehman, Z.U., Bajwa, I.S.: Lexicon-based sentiment analysis for Urdu language. In: 2016 Sixth International on Innovative Computing Technology (INTECH), Irealnd, UK/Islamabad. IEEE (2016)

    Google Scholar 

  12. Kumar, A.: Sentiment analysis on twitter. IJCSI Int. J. Comput. Sci. Issues 9(3), 372–378 (2012)

    Google Scholar 

  13. Kaur, J.: Emotion detection and sentiment analysis in text corpus: a differential study with informal and formal writing styles. Int. J. Comput. Appl. 101(9) (2014)

    Google Scholar 

  14. Kouloumpis, E., Wilson, T.: Twitter sentiment analysis: the good the bad and the OMG! In: ICWSM 2011, pp. 538–541 (2011)

    Google Scholar 

  15. Poria, S.: Fusing audio, visual and textual clues for sentiment analysis from multimodal content. Neurocomputing 174, 50–59 (2016)

    Google Scholar 

  16. Ingale, S.D.: Sentiment classification for product review analysis. Int. J. Eng. Res. Technol. (IJERT) 4, 646–650 (2015)

    Google Scholar 

  17. Saif, H.: Alleviating data sparsity for twitter sentiment analysis. In: CEUR Workshop Proceedings (CEUR-WS. org), pp. 2–9 (2012)

    Google Scholar 

  18. Rao, V.P.: Visualization of streaming data using social media. Imperial J. Interdisc. Res. 2(6) (2016)

    Google Scholar 

  19. Tang, H.: A survey on sentiment detection of reviews. Expert Syst. Appl. 36(7), 10760–10773 (2009)

    Google Scholar 

  20. Jamal, N.: Poetry classification using support vector machines. J. Comput. Sci. 8(9), 1441 (2012)

    Google Scholar 

  21. Lu, C.Y., Hsu, W.W.: Emotion sensing for internet chatting: a web mining approach for affective categorization of events. In: 13th International Conference on Computational Science and Engineering (CSE), pp. 295–301. IEEE (2010)

    Google Scholar 

  22. Bin, L., Jun, L.: Automated essay scoring using the KNN algorithm. In: International Conference on Computer Science and Software Engineering, vol. 1, pp. 735–738. IEEE (2008)

    Google Scholar 

  23. He, Z.S., Liang, W.T.: SVM-based classification method for poetry style. In: International Conference on Machine Learning and Cybernetics, vol. 5, pp. 2936–2940. IEEE (2007)

    Google Scholar 

  24. Pang, B., Lee, L.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the ACL 2002 Conference on Empirical Methods in Natural Language, vol. 10, pp. 79–86. Association for Computational Linguistics (2002)

    Google Scholar 

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Author information

Authors and Affiliations

  1. National College of Business Administration and Economics, Lahore, Pakistan

    Ateya Iram

Authors
  1. Ateya Iram
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Correspondence to Ateya Iram .

Editor information

Editors and Affiliations

  1. Department of Computer Science and IT, Islamia University of Bahawalpur, Baghdad, Pakistan

    Imran Sarwar Bajwa

  2. Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, UK

    Fairouz Kamareddine

  3. Department of Computer Engineering and Digital Systems, University of Sao Paulo, São Paulo, Brazil

    Anna Costa

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© 2019 Springer Nature Singapore Pte Ltd.

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Iram, A. (2019). Sentiment Analysis of Student’s Facebook Posts. In: Bajwa, I., Kamareddine, F., Costa, A. (eds) Intelligent Technologies and Applications. INTAP 2018. Communications in Computer and Information Science, vol 932. Springer, Singapore. https://doi.org/10.1007/978-981-13-6052-7_8

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  • DOI: https://doi.org/10.1007/978-981-13-6052-7_8

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6051-0

  • Online ISBN: 978-981-13-6052-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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