Sentiment Analysis of Nigerian Students’ Tweets on Education: A Data Mining Approach

Authors

  • Mayowa S. Alade Department of Computer Science, Nnamdi Azikiwe University, Awka
  • Joshua M. Nwankpa Department of Computer Science, Nnamdi Azikiwe University, Awka

Keywords:

Sentiment analysis, Naïve Bayes, Education, Students, Polarity, Twitter

Abstract

The paper is aimed at investigating data mining technologies by acquiring tweets from Nigerian University students on Twitter on how they feel about the current state of the Nigerian university system. The study for this paper was conducted in a way that the tweet data collected using the Twitter Application was pre-processed before being translated from text to vector representation using a feature extraction technique such Bag-of-Words. In the paper, the proposed sentiment analysis architecture was designed using UML and the Naïve Bayes classifier (NBC) approach, which is a simple but effective classifier to determine the polarity of the education dataset, was applied to compute the probabilities of the classes. Furthermore, Naïve Bayes classifier polarized the tweets' wording as negative or positive for polarity. Based on our investigation, the experiment revealed after data cleaning that 4016 of the total data obtained were utilized. Also, Positive attitudes accounted for 40.56%, while negative sentiments accounted for 59.44% of the total data having divided the dataset into 70:30 training and testing ratio, with the Naïve Bayes classifier being taught on the training set and its performance being evaluated on the test set. Because the models were trained on unbalanced data, we employed more relevant evaluation metrics such as precision, recall, F1-score, and balanced accuracy for model evaluation. The classifier's prediction accuracy, misclassification error rate, recall, precision, and f1-score were 63 %, 37%, 63%, 62%, and 62% respectively. All of the analyses were completed using the Python programming language and the Natural Language Tool Kit packages. Finally, the outcome of this prediction is the highest likelihood class. These forecasts can be used by Nigerian Government to improve the educational system and assist students to receive a better education.

References

Z. Kastrati, A. S. Imran, and A. Kurti, “Weakly Supervised Framework for Aspect-based Sentiment Analysis on Students’ reviews of MOOCs, IEEE Access, 8, 106799-106810, 2020, http://doi.10.1109/ACCESS.2020. 3000739.

D. Zimbra, H. Chen, and R. F. Lusch, “Stakeholder analyses of firm-related Web forums: Applications in stock return prediction”, ACM Transactions on Management Information Systems (TMIS), vol. 6, no. 1, pp. 1-38, 2015, https://doi.org/10.1145/2675693.

C, Forman, A. Ghose, and B. Wiesenfeld, “Examining the Relationship between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets”, Information systems research, vol. 19, no. 3, pp. 291-313, 2008, https://doi.org/10.1287/isre.1080.0193.

A. Tumasjan, T. Sprenger, P. Sandner, and I. Welpe, “Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment”, In Proceedings of the International AAAI Conference on Web and social media vol. 4, no. 1, pp. 178-185, 2010, https://ojs.aaai.org/index.php/ICWSM/article/view/14009.

A. M. Kaplan and M. Haenlein, “Users of the world, unite! The Challenges and Opportunities of social media”, Business Horizons, vol. 53, no. 1, pp. 59-68, 2010, https://doi.org/10.1016/j.bushor.2009.09.003.

N. G. Barnes and A. M. Lescault, “Social Media Adoption Soars as Higher-ed Experiments and Reevaluates its use of New Communications Tools”. Center for Marketing Research. University of Massachusetts Dartmouth, North Dartmouth, MA., 2011.

B. Pang and L. Lee, “Opinion Mining and Sentiment Analysis”, Foundations and Trends in information retrieval, vol. 2, no.1–2, pp. 1-135, 2008, http://dx.doi.org/10.1561/1500000011.

L. Balachandran and A. Kirupananda, “Online Reviews Evaluation System for Higher Education Institution: An Aspect based Sentiment Analysis Tool”, In 2017 11th international conference on software, knowledge, information management and applications (SKIMA), IEEE, 2017, pp. 1-7. https://doi: 10.1109/SKIMA.2017.8294118.

C. L. Santos, P. Rita, and J. Guerreiro, “Improving International Attractiveness of Higher Education Institutions based on Text Mining and Sentiment Analysis”, International Journal of Educational Management, vol. 32, no. 3, pp. 431-447, 2018, https://doi.org/10.1108/IJEM-01-2017-0027.

K. Z. Aung and N. N. Myo, “Sentiment Analysis of Students' Comment using Lexicon-based Approach”, In 2017 IEEE/ACIS 16th international conference on computer and information science (ICIS), pp. 149-154, 2017, https://doi: 10.1109/ICIS.2017.7959985.

F. F Balahadia, M. C. G. Fernando, and I. C. Juanatas, “Teacher's performance evaluation tool using opinion mining with sentiment analysis”, In 2016 IEEE region 10 symposium (TENSYMP), IEEE, pp.95-98, 2016, https:// doi: 10.1109/TENCONSpring.2016.7519384.

N. Altrabsheh, M. Cocea, and S. Fallahkhair, “Sentiment Analysis: Towards a Tool for Analysing Real-time Students Feedback.”. In 2014 IEEE 26th international conference on tools with artificial intelligence, IEEE, pp. 419-423. 2014, https:// doi: 10.1109/ICTAI.2014.70.

R. Cobos, F. Jurado, and Á. Villén, “Moods in MOOCs: Analyzing Emotions in the Content of Online Courses with edX-CA”, . In 2019 IEEE Global Engineering Education Conference (EDUCON), IEEE, pp. 1467-1474., 2019.

B. Batrinca and P. C. Treleaven, P. C. (2015) Social Media Analytics: A Survey of Techniques, Tools and Platforms. Ai & Society, vol.30, no.1, pp. 89-116, 2015, https://doi.org/10.1007/s00146-014-0549-4.

J. Claussen and C. Peukert, C. “Obtaining Data from the Internet: A Guide to Data Crawling in Management Research”. Available at SSRN 3403799, 2019. Available at SSRN: https://ssrn.com/abstract=3403799 or http://dx.doi.org/10.2139/ssrn.3403799.

X. Li, L. Bing, W. Zhang, and W. Lam, “Exploiting BERT for end-to-end Aspect-based Sentiment Analysis”, 2019, arXiv preprint arXiv:1910.00883, https://arxiv.org/abs/1910.00883

L. Yang, Y. Li, J. Wang, and R. S. Sherratt, “Sentiment Analysis for E-commerce Product Reviews in Chinese based on Sentiment Lexicon and Deep Learning”, IEEE access, 8, pp. 23522-23530, 2020.

S. Rani and P. Kumar, “A Sentiment Analysis System to Improve Teaching and Learning”, Computer, vol. 50, no. 5, pp. 36-43., 2017, https://doi: 10.1109/MC.2017.133.

A. Kaur and H. Kaur, “Framework for Opinion Mining Approach to Augment Education System Performance”, arXiv Preprint arXiv:1806.09279, 8(6)., 2018. https://doi.org/10.48550/arXiv.1806.09279 .

E. Cambria, B. Schuller, Y. Xia, and C. Havasi, “New Avenues in Opinion Mining and Sentiment Analysis”, IEEE Intelligent systems, vol. 28, no. 2, pp. 15-21, 2013.

K. Lundqvist, T. Liyanagunawardena, and L. Starkey, “Evaluation of Student Feedback within a MOOC using Sentiment Analysis and Target Groups”, International Review of Research in Open and Distributed Learning, vol. 21, no. 3, pp. 140-156., 2020, https://doi.org/10.19173/irrodl.v21i3.4783.

A. Onan, (2021) Sentiment analysis on Product Reviews Based on Weighted Word Embeddings and Deep Neural Networks. Concurrency and Computation: Practice and Experience, vol. 33, no. 23, 2021, e5909. https://doi.org/10.1002/cpe.5909.

S. S. Mukku, N. Choudhary, and R. Mamidi, “Enhanced sentiment classification of Telugu text using ml techniques”, In SAAIP@ IJCAI, 2016.

I. A. Kandhro, M. A. Chhajro, K. Kumar, H. N. Lashari, and U. Khan, U. (2019) Student Feedback sentiment analysis model using various machine learning schemes: A review. Indian Journal of Science and Technology, vol.12, no. 14, pp.1-9, 2019, https://doi:10.17485/ijst/2019/v12i14/143243.

W. Medhat, A. Hassan, and H. Korashy, “Sentiment Analysis Algorithms and Applications: A survey”, Ain Shams engineering journal, vol. 5, no. 4, pp. 1093-1113, 2014, https://doi.org/10.1016/j.asej.2014.04.011

G. Vinodhini and R. M. Chandrasekaran, “Sentiment Analysis and Opinion Mining: A Survey”. International Journal, vol. 2, no. 6, pp. 282-292., 2012, Available at http://www.ijarcsse.com

A. Jeyapriya and C. K. Selvi, “Extracting Aspects and Mining Opinions in Product Reviews using Supervised Learning Algorithm”, In 2015 2nd international conference on electronics and communication systems (ICECS), IEEE, pp. 548-552., 2015, https://doi: 10.1109/ECS.2015.7124967.

R. Menaha, R. Dhanaranjani, T. Rajalakshmi, and R. Yogarubini, “Student Feedback Mining System using Sentiment Analysis”, International Journal of Computer Applications Technology and Research, vol. 6, no. 1, pp. 51-55, 2017, http://www.ijcat.com

B. Pang and L. Lee, Opinion Mining and Sentiment Analysis. Foundations and Trends® in information retrieval, vol. 2, no. 1–2, 1-135, 2008, https:// doi: 10.1109/ECS.2015.7124967.

É. Guàrdia-Sebaoun, A. Rafrafi, V. Guigue, and P. Gallinari, “Cross-media Sentiment Classification and Application to box-office Forecasting”. In OAIR, pp. 201-208, 2013.

A. E. O. Carosia, G. P. Coelho, and A. E. A. Silva, “Analyzing the Brazilian Financial Market through Portuguese Sentiment Analysis in social media”, Applied Artificial Intelligence, vol. 34, no. 1, pp. 1-19, 2020, https://doi.org/10.1080/08839514.2019.1673037.

A. Rashid, S. Asif, N. A. Butt, and I. Ashraf, “Feature level opinion mining of educational student feedback data using sequential pattern mining and association rule Mining”, International Journal of Computer Applications, vol. 81, no. 10, pp. 31-38, 2013.

V. Dhanalakshmi, D. Bino, and A. M. Saravanan, “Opinion mining from student feedback data using supervised learning algorithms”, In 2016 3rd MEC international conference on big data and smart city (ICBDSC) pp. 1-5, 2016. IEEE.

C. K. Leong, Y. H. Lee, and W. K. Mak, W. K, “Mining sentiments in SMS texts for teaching evaluation”, Expert Systems with Applications, vol.39, no.3, pp. 2584-2589, 2012.

https://doi.org/10.1016/j.eswa.2011.08.113.

B. Jagtap, and V. Dhotre, (2014). SVM and HMM based hybrid approach of sentiment analysis for teacher feedback assessment. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), 3(3), 229-232, 2014.

N. Altrabsheh, M. M. Gaber, and M. Cocea, “SA-E: sentiment analysis for education”, In International conference on intelligent decision technologies, Vol. 255, pp. 353-362, 2013.

J. Sultana, N. Sultana, K. Yadav, and F AlFayez, “Prediction of sentiment analysis on educational data based on deep learning approach”, In 2018 21st Saudi computer society national computer conference (NCC) pp. 1-5, 2018, IEEE.

G. G Esparza, A. de-Luna, A. O. Zezzatti, A. Hernandez, J. Ponce, M. Álvarez, E. Cossio, and J. D. Jesus Nava, “A Sentiment Analysis Model to Analyze Students Reviews of Teacher Performance using Support Vector Machines”, In International Symposium on Distributed Computing and Artificial Intelligence, Springer, Cham, pp. 157-164, 2017, https://link.springer.com/chapter/10.1007/978-3-319-62410-5_19

Q. Rajput, S. Haider, and S. Ghani, “Lexicon-based Sentiment Analysis of Teachers’ Evaluation”, Applied computational intelligence and soft computing, 2016, https://doi.org/10.1155/2016/2385429

E. ?skender and G. B. Bat?, “Comparing Turkish Universities Entrepreneurship and Innovativeness Index's Rankings with Sentiment Analysis Results on social media”, Procedia-Social and Behavioral Sciences, vol. 195, pp. 1543-1552, 2015, https://doi.org/10.1016/j.sbspro.2015.06.457.

A. Pak, and P. Paroubek, “Twitter as a corpus for sentiment analysis and opinion mining”, In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10). 2010, http://www.lrec-conf.org/proceedings/lrec2010/pdf/385_Paper.pdf.

A. Bifet, and E. Frank, “Sentiment knowledge discovery in twitter streaming data”, In International conference on discovery science, pp. 1-15, 2010, Springer, Berlin, Heidelberg.

P. Kaewyong, A. Sukprasert, N. Salim, and F. A. Phang,” The Possibility of Students’ Comments Automatic Interpret using Lexicon based Sentiment Analysis to Teacher Evaluation”, In 3rd International Conference on Artificial Intelligence and Computer Science (AICS2015), pp. 179-189, 2015.

K. Mite-Baidal, C. Delgado-Vera, E. Solís-Avilés, A. H. Espinoza, J. Ortiz-Zambrano, and E. Varela-Tapia, “Sentiment Analysis in Education Domain: A Systematic Literature Review”, In: Valencia-García, R., Alcaraz-Mármol, G., Del Cioppo-Morstadt, J., Vera-Lucio, N., Bucaram-Leverone, M. (eds) Technologies and Innovation. CITI 2018. Communications in Computer and Information Science, Springer, Cham, vol. 883, no. 2, pp. 85–297, 2018, https://doi.org/10.1007/978-3-030-00940-3_21.

Z. Han, J. Wu, C. Huang, Q. Huang, and M. Zhao, “A Review on Sentiment Discovery and Analysis of Educational Big?Data”, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 10, no. 1, 2020, https://doi.org/10.1002/widm.1328.

J. Zhou and J. M. Ye, “Sentiment Analysis in Education Research: A Review of Journal publications”, Interactive learning environments, pp. 1-13, 2020, https://doi.org/10.1080/10494820.2020.1826985

L. Amusa, W. Yahya, and A. Balogun, “Data Mining of Nigerian’s Sentiments on the Administration of Federal Government of Nigeria”, Annals. Computer Science Series, vol. 14, no. 2, pp. 69-75, 2016.

R. Baragash and H. Aldowah, “Sentiment Analysis in Higher Education: A Systematic Mapping Review”, International Conference on Applied and Practical Sciences (ICAPS), Journal of Physics: Conference Series, 1860, pp. 1-14, 2021.

R. K. Jena, (“Sentiment Mining in a Collaborative Learning Environment: Capitalising on Big Data”, Behaviour & Information Technology, vol. 38, no. 9, pp. 986-1001, https://doi.org/10.1080/0144929X.2019. 1625440.

Twitter, 2016). Company | About." Twitter. Twitter, 30 June 2016.

I. Hemalatha, G. S. Varma, and A. Govardhan, “Preprocessing the Informal Text for Efficient Sentiment Analysis”, International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), vol.1, no.2, pp. 58-61, 2012, Available at www.ijettcs.org.

A. MacArthur, “The Real History of Twitter. Brief. How the micro messaging wars were won”, 2018, Retrieved fromhttp://twitter.about.com/od/Twitter-Basics/a/The-Real-History-of-Twitter-In-Brief.htm [July 23, 2022]

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Published

2022-10-02

How to Cite

Mayowa S. Alade, & Joshua M. Nwankpa. (2022). Sentiment Analysis of Nigerian Students’ Tweets on Education: A Data Mining Approach. International Journal of Computer (IJC), 45(1), 1–27. Retrieved from https://www.ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/1971

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