Sentiment Analysis on Erspo Jersey in X Using Machine Learning Algorithms

Andi Asrida Reskinah. D; Marhawati Najib; Muhammad Ashdaq

doi:10.58905/saga.v2i3.334

Authors

Andi Asrida Reskinah. D Department of Economic Education, Universitas Negeri Makassar, Makassar, Indonesia
Marhawati Najib Department of Digital Business, Universitas Negeri Makassar, Makassar, Indonesia
Muhammad Ashdaq Department of Digital Business, Universitas Negeri Makassar, Makassar, Indonesia

DOI:

https://doi.org/10.58905/saga.v2i3.334

Keywords:

Sentiment Analysis, Machine Learning, Erspo Jersey, X

Abstract

This research conducts a sentiment analysis on Erspo jerseys using machine learning algorithms on the X platform. The objective is to identify the public's sentiment and compare the performance of three algorithms: Naïve Bayes, K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). Data was collected through web scraping of tweets between January and September 2024, containing keywords related to Erspo. Using a lexicon-based approach, the preprocessing steps involved cleaning, tokenizing, normalizing, and labeling data into positive, negative, and neutral sentiments. Results show that the Naïve Bayes algorithm provided the highest accuracy in sentiment classification, followed by SVM and KNN. Positive sentiment primarily centered on product loyalty, while negative sentiment largely criticized jersey design and quality. The findings offer important insights for Erspo stakeholders to refine marketing strategies and product improvements. This study highlights the potential of machine learning in analyzing consumer opinions at scale, making it a valuable tool for real-time consumer feedback analysis.

References

We Are Social 2023 Social Media Marketing Benchmark Report 2024 Influencer Marketing Hub

Diakses pada September 2023 dari https://influencermarketinghub.com/social-media-marketing-benchmark-report/

Putri D, Nama G F, Sulistiono W E 2022 Analisis Sentimen Kinerja Dewan Perwakilan Rakyat (DPR) Pada Twitter Menggunakan Metode Naive Bayes Classifier Jurnal Informatika Dan Teknik Elektro Terapan 10 34-40 https://doi.org/10.23960/jitet.v10i1.2262

Verawati I, Audit B S 2022 Algoritma Naïve Bayes Classifier Untuk Analisis Sentimen Pengguna Twitter Terhadap Provider By.U Jurnal Media Informatika Budidarma 6 1411 https://doi.org/10.30865/mib.v6i3.4132

Zhang Chen, Liu Xi 2020 A Distributed Storage And Computation K-Nearest Neighbor Algorithm Based Cloud-Edge Computing For Cyber-Physical-Social-Systems IEEE Access https://doi.org/10.1109/Access.2020.297476

Kumar R et al. 2023 Sentiment Analysis of Government Policies Using SEMMA Method Journal ABC

Brahimi B, Touahria M, Tari A 2021 Improving sentiment analysis in Arabic: A combined approach Journal of King Saud University - Computer and Information Sciences 33 1242-1250 https://doi.org/10.1016/j.jksuci.2019.07.011

Wicaksono N B A 2022 Analisis sentimen tingkat kepuasan pengguna BPJS dengan metode SentiStrength Jurnal Ilmiah Intech: Information Technology Journal of UMUS 4 189-199 https://doi.org/10.46772/intech.v4i02.868

Irnawati O, Solecha K 2022 Analisis sentimen ulasan aplikasi Flip menggunakan Naïve Bayes dengan seleksi fitur PSO Jurnal Ilmiah Intech: Information Technology Journal of UMUS 4 189-199 https://doi.org/10.46772/intech.v4i02.868

Moleong L J 2016 Metodologi penelitian kualitatif (Revised ed.) PT Remaja Rosdakarya

Arikunto S 2014 Prosedur penelitian: Suatu pendekatan praktik (Revised ed.) Rineka Cipta

Larose D T, Larose C D 2015 Data Mining and Predictive Analytics John Wiley & Sons

Han J, Kamber M, Pei J 2011 Data Mining: Concepts and Techniques Morgan Kaufmann

Hastie T, Tibshirani R, Friedman J 2009 The Elements of Statistical Learning: Data Mining, Inference, and Prediction Springer

Witten I H, Frank E, Hall M A 2011 Data Mining: Practical Machine Learning Tools and Techniques Morgan Kaufmann

Saito T, Rehmsmeier M 2015 The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets PLOS ONE 10 e0118432 https://doi.org/10.1371/journal.pone.0118432

Davis J, Goadrich M 2006 The relationship between Precision-Recall and ROC curves Proceedings of the 23rd international conference on Machine learning 233-240

He H, Garcia E A 2009 Learning from imbalanced data IEEE Transactions on Knowledge and Data Engineering 21 1263-1284

Liu B 2015 Sentiment analysis: Mining opinions, sentiments, and emotions Cambridge University Press http://ejournal.uajy.ac.id/27165/http://ejournal.uajy.ac.id/27165/3/150708283_Bab2.pdf