Sentiment Analysis of University Social Media Using Support Vector Machine and Logistic Regression Methods
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Abstract
Social media has become one of the most powerful platforms for information sharing. Colleges and universities now have official social media profiles to convey information about the campus and boost its branding and popularity. Instagram is a popular social networking website among college students. It is important for a university to comprehend its performance from the community's perspective, whether positive, negative, or indifferent toward the university. One solution is to examine the university's social media sentiment to establish the public's perception of the university. In this study, we will conduct a sentiment analysis on university social media based on public opinion or comments for each post on the university's Instagram to identify whether the comments are “Positive,†“Negative,†or “Neutral.†To classify posts on university Instagram, we use two methods: Support Vector Machine and Logistic Regression. The results suggest combining the Support Vector Machine approach with the TF-IDF feature yields the best F1-Score performance. In contrast, Logistic Regression with the FastText feature produces the worst performance of all models and feature extraction employed.
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References
[2] X. Fang and J. Zhan, “Sentiment analysis using product review data,†J. Big Data, vol. 2, no. 1, p. 5, Jun. 2015, doi: 10.1186/s40537-015-0015-2.
[3] H. Kang, S. J. Yoo, and D. Han, “Senti-lexicon and improved Naïve Bayes algorithms for sentiment analysis of restaurant reviews,†Expert Syst. Appl., vol. 39, no. 5, pp. 6000–6010, 2012, doi: https://doi.org/10.1016/j.eswa.2011.11.107.
[4] F. Neri, C. Aliprandi, F. Capeci, M. Cuadros, and T. By, “Sentiment Analysis on Social Media,†Aug. 2012.
[5] A. Abdelrazeq, D. Janßen, C. Tummel, S. Jeschke, and A. Richert, “Sentiment analysis of social media for evaluating universities,†in Automation, Communication and Cybernetics in Science and Engineering 2015/2016, Springer, 2016, pp. 233–251.
[6] S. Sazzed and S. Jayarathna, “SSentiA: A Self-supervised Sentiment Analyzer for classification from unlabeled data,†Mach. Learn. with Appl., vol. 4, p. 100026, Jun. 2021, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2666827021000074
[7] M. Adnan, R. Sarno, and K. R. Sungkono, “Sentiment Analysis of Restaurant Review with Classification Approach in the Decision Tree-J48 Algorithm,†2019 Int. Semin. Appl. Technol. Inf. Commun., pp. 121–126, 2019.
[8] S. Garc\’\ia, J. Luengo, and F. Herrera, Data pre-processing in data mining, vol. 72. Springer, 2015.
[9] H. Hamdan, P. Bellot, and F. Bechet, “Lsislif: Crf and logistic regression for opinion target extraction and sentiment polarity analysis,†in Proceedings of the 9th international workshop on semantic evaluation (SemEval 2015), 2015, pp. 753–758.
[10] N. Zainuddin and A. Selamat, “Sentiment analysis using support vector machine,†in 2014 international conference on computer, communications, and control technology (I4CT), 2014, pp. 333–337.
[11] J. Han, M. Kamber, and J. Pei, “8 - Classification: Basic Concepts,†in Data Mining (Third Edition), Boston: Morgan Kaufmann, 2012, pp. 327–391. [Online]. Available: https://www.sciencedirect.com/science/article/pii/B9780123814791000083
[12] B. Trstenjak, S. Mikac, and D. Donko, “KNN with TF-IDF based framework for text categorization,†Procedia Eng., vol. 69, pp. 1356–1364, 2014.
[13] K. Soumya George and S. Joseph, “Text classification by augmenting bag of words (BOW) representation with co-occurrence feature,†IOSR J. Comput. Eng, vol. 16, no. 1, pp. 34–38, 2014.
[14] O. Levy and Y. Goldberg, “Dependency-based word embeddings,†in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2014, pp. 302–308.
[15] T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,†arXiv Prepr. arXiv1301.3781, 2013.
[16] P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov, “Enriching word vectors with subword information,†Trans. Assoc. Comput. Linguist., vol. 5, pp. 135–146, 2017.
[17] E. Grave, P. Bojanowski, P. Gupta, A. Joulin, and T. Mikolov, “Learning Word Vectors for 157 Languages,†2018.
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