Sentiment Analysis of Flagship Smartphones on Social Media Using Python TextBlob And Naive Bayes Algorithm

Alif Ilman Naifan, Muhammad Farhan Fauzaan, Riyandi Firman Pratama

Abstract


Social media plays a crucial role in the advancement of organizations, industries, and businesses nowadays. Almost everyone is connected to social media. Each individual can interact and exchange knowledge due to the fusion of technology and social relationships. Sentiment analysis is a technique that allows extracting information from users expressing emotions, perspectives, and opinions on the internet. One strategic sector for implementing sentiment analysis is the technology sector, especially the smartphone industry. The wide range of smartphone variants available today poses a problem for individuals in finding the best smartphone product. The sentiment analysis of flagship smartphones conducted in this article aims to find the best solution between two flagship smartphones from renowned manufacturers, namely the Samsung S22 Ultra and the Xiaomi 12 Pro. The data is collected from various social media platforms such as Twitter, YouTube, and GSMArena. The collected data is then analyzed using Python TextBlob, and the analysis results in negative, positive, and neutral sentiments displayed through various visualizations. The final outcome is the assessment of Net Brand Reputation, which evaluates the reputation of a brand across multiple social media platforms.

Keywords


Flagship smartphone; Naïve bayes; Python; Social media; TextBlob

Full Text:

PDF

References


Astari, N. K. P., Divayana, D. G. H., and Indrawan, G. (2020). Analisis sentimen dokumen twitter mengenai dampak virus corona menggunakan metode naive bayes classifier. JSI (Jurnal Sistem Dan Informatika), 15(1), 27–29.

Bianchi, C., & Andrews, L. (2015). Investigating marketing managers' perspectives on social media in Chile. Journal of Business Research, 68(12), 2552-2559.

Christina, S., and Ronaldo, D. (2018). A survey of sentiment analysis using sentiwordnet on bahasa indonesia. Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika, 12(2), 69-73.

Dawid, A. P., & Skene, A. M. (1979). Maximum likelihood estimation of observer error‐rates using the EM algorithm. Journal of the Royal Statistical Society: Series C (Applied Statistics), 28(1), 20-28.

De Haes, S., & Van Grembergen, W. (2009). An exploratory study into IT governance implementations and its impact on business/IT alignment. Information Systems Management, 26(2), 123-137.

Hayuningtyas, R. Y. (2019). Penerapan algoritma naïve bayes untuk rekomendasi pakaian wanita. Jurnal Informatika, 6(1), 18–22.

Hidayatullah, A., Marzuki, I., & Aprilia, I. (2020). Rancang Bangun Aplikasi Web Crawling Dengan Fitur Pencarian Berita Yang Relevan Berbasis TF-IDF. Energy-Jurnal Ilmiah Ilmu-Ilmu Teknik, 10(2), 114-120.

Jadhav, S. D., and Channe, H. P. (2016). Comparative study of k-nn, naive bayes and decision tree classification techniques. International Journal of Science and Research (IJSR), 5(1), 1842-1845.

Kalaivani, E. R., & Marivendan, E. R. (2021). The Effect of Stop Word Removal and Stemming In Datapreprocessing. Annals of the Romanian Society for Cell Biology, 25(6), 739-746.

Kotsiantis, S. B., Kanellopoulos, D., & Pintelas, P. E. (2006). Data preprocessing for supervised leaning. International journal of computer science, 1(2), 111-117.

Muntinga, D. G., Moorman, M., & Smit, E. G. (2011). Introducing COBRAs: Exploring motivations for brand-related social media use. International Journal of advertising, 30(1), 13-46.

Nirawana, I. Wayan Sugianta, and Indrawan, I. Gede. (2016). Analisis sentimen pada review film dengan menggunakan algoritma klasifikasi naive bayes berdasarkan term objects keywords. Semin. Nas. Pendidik. Tek. Inform.(SENAPATI 2016), (no. Senapati), 172-174.

Nurkholis, A., Alita, D., & Munandar, A. (2022). Comparison of Kernel Support Vector Machine Multi-Class in PPKM Sentiment Analysis on Twitter. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 6(2), 227-233.

Pudjajana, A. M., and Manongga, D. (2018). Sentimen analisis tweet pornografi kaum homoseksual indonesia di twitter dengan naive bayes. Simetris: Jurnal Teknik Mesin, Elektro Dan Ilmu Komputer, 9(1), 313–318.

Ridwan, M., Suyono, H., and Sarosa, M. (2013). Penerapan data mining untuk evaluasi kinerja akademik mahasiswa menggunakan algoritma naive bayes classifier. Jurnal EECCIS, 7(1), 59–64.

Septian, J., Fachrudin, T., and Nugroho, A. A. (2019). Analisis sentimen pengguna twitter terhadap polemik persepakbolaan indonesia menggunakan pembobotan tf-idf dan k-nearest neighbor. Insyst, 1(1), 43–49.

Utami, R. W., Jazuli, A., and Khotimah, T. (2021). Analisis sentimen terhadap xiaomi Indonesia menggunakan metode naïve bayes. Indonesian Journal of Technology, Informatics and Science (IJTIS), 3(1), 21-30.

Vijayarani, S., & Janani, R. (2016). Text mining: open source tokenization tools-an analysis. Advanced Computational Intelligence: An International Journal (ACII), 3(1), 37-47.

Wong, K. H., Chang, H. H., & Yeh, C. H. (2019). The effects of consumption values and relational benefits on smartphone brand switching behavior. Information Technology & People, 32(1), 217-243.

Zahidi, Y., El Younoussi, Y., and Al-Amrani, Y. (2021). Different valuable tools for arabic sentiment analysis: a comparative evaluation. International Journal of Electrical and Computer Engineering, 11(1), 2088-8708.




DOI: https://doi.org/10.17509/seict.v4i1.59633

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Journal of Software Engineering, Information and Communication Technology (SEICT)

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Journal of Software Engineering, Information and Communicaton Technology (SEICT), 
(e-ISSN:2774-1699 | p-ISSN:2744-1656) published by Program Studi Rekayasa Perangkat Lunak, Kampus UPI di Cibiru.


 Indexed by.