Sentiment Analysis of Brand Ambassador Influence on Product Buyer Interest Using KNN and SVM

Natasya Kurnia Putri, Anik Vega Vitianingsih, Slamet Kacung, Anastasia Lidya Maukar, Verdi Yasin

Abstract


In the dynamic marketing, companies usually use strategies involving celebrities or influencers to promote their products or brands. The currently popular strategy is using Korean boy bands as brand ambassadors. This collaboration certainly gets a lot of opinion responses through tweets on X app social media. This research aims to analyze sentiment to determine how the product buyer's interest responds to brand suitability, brand image management, and the influence of issues that arise in this collaboration. The research stages consist of data collection, pre-processing, labeling, weighting, and classification with K-Nearest Neighbor and Support Vector Machine and performance evaluation using a confusion matrix. The dataset used was 696 tweets taken using web scrapping techniques. This research uses the Lexicon-based method to divide the dataset into positive, negative, and neutral classes. The SVM method shows superior test results by achieving an accuracy rate of 83.34% compared to the KNN method, which produces an accuracy value of 71.2% in its calculations

Keywords


Brand Ambassador Influence; K-Nearest Neighbor; Product Buyer Interest; Sentiment Analysis; Support Vector Machine

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References


P. K. P. D. B. E. T. K. PELANGGAN, “Pengaruh Kualitas Pelayanan dan Promosi,” Dahmiri, vol. 2, no. 2, pp. 183–192, 2018.

N. Nurhasanah, Z. P. Febriyani, and P. SK, "Pengaruh brand ambassador dan social media ads terhadap purchase intention melalui brand image produk skincare ms glow," J. Ris. Ekon. dan Bisnis, vol. 16, no. 2, p. 156, 2023, doi: 10.26623/jreb.v16i2.6466.

Dessy Angelina, U. Hayati, and G. Dwilestari, “Penerapan Metode Support Vector Machine Pada Sentimen Analisis Pengguna Twitter Terhadap Konser K-Pop,” Kopertip J. Ilm. Manaj. Inform. dan Komput., vol. 7, no. 1, pp. 14–23, 2023, doi: 10.32485/kopertip.v7i1.251.

D. A. Yusuf, A. L. Tumbel, and D. Woran, “Pengaruh Kualitas Produk Dan Brand Ambassador Kpop Nct Dream Terhadap Keputusan Pembelian Pada Produk Mie Lemonilo Di Manado,” J. EMBA J. Ris. Ekon. Manajemen, Bisnis dan Akunt., vol. 10, no. 3, p. 965, 2022, doi: 10.35794/emba.v10i3.43526.

W. Yulita, “Analisis Sentimen Terhadap Opini Masyarakat Tentang Vaksin Covid-19 Menggunakan Algoritma Naïve Bayes Classifier,” J. Data Min. dan Sist. Inf., vol. 2, no. 2, p. 1, 2021, doi: 10.33365/jdmsi.v2i2.1344.

Y. Cahyono and S. Saprudin, “Analisis Sentiment Tweets Berbahasa Sunda Menggunakan Naive Bayes Classifier dengan Seleksi Feature Chi Squared Statistic,” J. Inform. Univ. Pamulang, vol. 4, no. 3, p. 87, 2019, doi: 10.32493/informatika.v4i3.3186.

R. I. Pristiyanti, M. A. Fauzi, and L. Muflikhah, “Sentiment Analysis Peringkasan Review Film Menggunakan Metode Information Gain dan K-Nearest Neighbor,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 3, pp. 1179–1186, 2018, [Online]. Available: http://j-ptiik.ub.ac.id

U. Khaira, R. Johanda, P. E. P. Utomo, and T. Suratno, "Sentiment Analysis Of Cyberbullying On Twitter Using SentiStrength," Indones. J. Artif. Intell. Data Min., vol. 3, no. 1, p. 21, 2020, doi: 10.24014/ijaidm.v3i1.9145.

S. Rabbani, D. Safitri, F. Try Puspa Siregar, R. Rahmaddeni, and L. Efrizoni, "Evaluation of Support Vector Machine, Naive Bayes, Decision Tree, and Gradient Boosting Algorithms for Sentiment Analysis on ChatGPT Twitter Dataset," Indones. J. Artif. Intell. Data Min., vol. 7, no. 1, p. 11, 2023, doi: 10.24014/ijaidm.v7i1.24662.

A. Mustolih, P. Arsi, and P. Subarkah, "Sentiment Analysis Motorku X Using Applications Naive Bayes Classifier Method," Indones. J. Artif. Intell. Data Min., vol. 6, no. 2, p. 231, 2023, doi: 10.24014/ijaidm.v6i2.24864.

R. Slamet, W. Gata, A. Novtariany, K. Hilyati, F. Ainun Jariyah, and U. Nusa Mandiri, “Twitter Sentiment Analysis of South Korea Artists As Brand Ambassadors of Local Beauty Products,” J. Inf. Technol. Comput. Sci., vol. 5, no. 1, 2022.

Regina, T. H. Saragih, and D. Kartini, “Analisis Sentimen Brand Ambassador Bts Terhadap Tokopedia Menggunakan Klasifikasi Bayesian Network Dengan Ekstraksi Fitur Tf-Idf,” J. Inform. Polinema, vol. 9, no. 4, pp. 383–390, 2023, doi: 10.33795/jip.v9i4.1333.

D. Musfiroh, U. Khaira, P. E. P. Utomo, and T. Suratno, “Analisis Sentimen terhadap Perkuliahan Daring di Indonesia dari Twitter Dataset Menggunakan InSet Lexicon,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 1, no. 1, pp. 24–33, 2021, doi: 10.57152/malcom.v1i1.20.

T. Pano and R. Kashef, "A complete vader-based sentiment analysis of bitcoin (BTC) tweets during the ERA of COVID-19," Big Data Cogn. Comput., vol. 4, no. 4, pp. 1–17, 2020, doi: 10.3390/bdcc4040033.

T. Mustaqim, K. Umam, and M. A. Muslim, "Twitter text mining for sentiment analysis on government's response to forest fires with vader lexicon polarity detection and k-nearest neighbor algorithm," J. Phys. Conf. Ser., vol. 1567, no. 3, 2020, doi: 10.1088/1742-6596/1567/3/032024.

E. Cambria, Q. Liu, S. Decherchi, F. Xing, and K. Kwok, "SenticNet 7: A Commonsense-based Neurosymbolic AI Framework for Explainable Sentiment Analysis," 2022 Lang. Resour. Eval. Conf. Lr. 2022, no. June, pp. 3829–3839, 2022.

S. Smetanin, "The Applications of Sentiment Analysis for Russian Language Texts: Current Challenges and Future Perspectives," IEEE Access, vol. 8, pp. 110693–110719, 2020, doi: 10.1109/ACCESS.2020.3002215.

A. S. Rizki, A. Tjahyanto, and R. Trialih, "Comparison of stemming algorithms on Indonesian text processing," Telkomnika (Telecommunication Comput. Electron. Control., vol. 17, no. 1, pp. 95–102, 2019, doi: 10.12928/TELKOMNIKA.v17i1.10183.

Y. Asri, W. N. Suliyanti, D. Kuswardani, and M. Fajri, “Pelabelan Otomatis Lexicon Vader dan Klasifikasi Naive Bayes dalam menganalisis sentimen data ulasan PLN Mobile,” Petir, vol. 15, no. 2, pp. 264–275, 2022, doi: 10.33322/petir.v15i2.1733.

A. Apriani, H. Zakiyudin, and K. Marzuki, “Penerapan Algoritma Cosine Similarity dan Pembobotan TF-IDF System Penerimaan Mahasiswa Baru pada Kampus Swasta,” J. Bumigora Inf. Technol., vol. 3, no. 1, pp. 19–27, 2021, doi: 10.30812/bite.v3i1.1110.

R. Hidayat, Cara Praktis Membangun Website Gratis. Jakarta: PT. Elex Media Komputindo, 2010.

J. A. Septian, T. M. Fachrudin, and A. Nugroho, “Analisis Sentimen Pengguna Twitter Terhadap Polemik Persepakbolaan Indonesia Menggunakan Pembobotan TF-IDF dan K-Nearest Neighbor,” J. Intell. Syst. Comput., vol. 1, no. 1, pp. 43–49, 2019, doi: 10.52985/insyst.v1i1.36.

K. Pramayasa, I. M. D. Maysanjaya, and I. G. A. A. D. Indradewi, “Analisis Sentimen Program Mbkm Pada Media Sosial Twitter Menggunakan KNN Dan SMOTE,” SINTECH (Science Inf. Technol. J., vol. 6, no. 2, pp. 89–98, 2023, doi: 10.31598/sintechjournal.v6i2.1372.

Ferdi and Vina Ayumi, “Analisa Sentimen Mengenai Kenaikan Harga Bbm Menggunakan Metode Naïve Bayes Dan Support Vector Machine,” JSAI (Journal Sci. Appl. Informatics), vol. 6, no. 1, pp. 1–10, 2023, doi: 10.36085/jsai.v6i1.4628.

A. Setiawan, Zunan., Fajar, Muhammad., Priyatno, Arif Mudi., Putri, Anggi Yhurinda Perdana., Aryuni, Mediana., Yulianti, Siti., Junaidi, Satrio., Wijaya, Buku Ajar DATA MINING.

N. M. Farhan and B. Setiaji, “Analisis Sentimen Ulasan Aplikasi TikTok Shop Seller Center di Google Playstore Menggunakan Algoritma Naive Bayes,” Indones. J. Comput. Sci., vol. 12, no. 2, pp. 284–301, 2023.




DOI: http://dx.doi.org/10.24014/ijaidm.v7i2.29469

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