Sentiment Analysis of Food Order Tweets to Find Out Demographic Customer Profile Using SVM

  • Syahril Efendi Universitas Sumatera Utara
  • Poltak Sihombing Universitas Sumatra Utara
Keywords: Classification, Demographic Profile, Machine Learning, Sentiment Analysis, Tweets


The use of online food ordering through food systems or applications continues to increase, requiring vendors to implement marketing and sales strategies through surveys, feedback. The problems that arise are building a system analysis model from a collection of tweets with hashtags or usernames for ordering food online . The Support Vector Machine (SVM) algorithm is used for text classification. Tweets are collected into data sets, training data, and testing data, then a classification model of the SVM Algorithm is built. Preprocessing data, tweets are cleansing, tokenized, and stopword remove. From the collected tweets, they are grouped into 10 variables to identify demographic profiles. The results of the analysis are classified as positive sentiments, namely residence, price range, using promos, paid types, halal food while negative sentiments are ethnicity, culture, vegetarianism, place. Classification accuracy is important to validate the results of the SVM model. From 500 train data tweet, the resulting classification is 66% positive sentiment and 34% negative sentiment. Overall accuracy model Linier SVM result 83.2% with accuracy 92.55%.


Download data is not yet available.


[1] F. D. Ananda and Y. Pristyanto, “Analisis Sentimen Pengguna Twitter Terhadap Layanan Internet Provider Menggunakan
Algoritma Support Vector Machine,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 20, no. 2,
pp. 407–416, 2021.
[2] W. Bourequat and H. Mourad, “Sentiment Analysis Approach for Analyzing Iphone Release Using Support Vector Machine,”
International Journal of Advances in Data and Information Systems, vol. 2, no. 1, pp. 36–44, 2021.
[3] S. Dey, S. Wasif, D. S. Tonmoy, S. Sultana, J. Sarkar, and M. Dey, “A Comparative Study of Support Vector Machine and
Naive Bayes Classifier for Sentiment Analysis on Amazon Product Reviews,” 2020 International Conference on Contemporary
Computing and Applications, IC3A 2020, no. May, pp. 217–220, 2020.
[4] A. N. Rosyad, R. Syarief, and M. F. Syuaib, “Business Model Development Strategy for Frozen Food Micro-Business in The
New Normal Era (Case Study : CV XYZ) ,” vol. 8, no. 1, pp. 93–103, 2022.
[5] C. Villavicencio, J. J. Macrohon, X. A. Inbaraj, J. H. Jeng, and J. G. Hsieh, “Twitter Sentiment Analysis Towards Covid-19
Vaccines in The Philippines Using Na¨ıve Bayes,” Information (Switzerland), vol. 12, no. 5, 2021.
[6] M. Boukabous and M. Azizi, “Crime Prediction using A Hybrid Sentiment Analysis Approach Based on The Bidirectional
Encoder Representations From Transformers,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 25,
no. 2, pp. 1131–1139, 2022.
[7] E. M. Sari Rochman, I. Oktavia Suzanti, Imamah, M. Ali Syakur, D. R. Anamisa, A. Khozaimi, and A. Rachmad, “Classification
of Thesis Topics Based on Informatics Science Using SVM,” IOP Conference Series: Materials Science and Engineering, vol.
1125, no. 1, p. 012033, 2021.
[8] R. Amalia, M. A. Bijaksana, and D. Darmantoro, “Negation Handling in Sentiment Classification Using Rule-Based Adapted
From Indonesian Language Syntactic for Indonesian Text in Twitter,” Journal of Physics: Conference Series, vol. 971, no. 1,
[9] A. Faesal, A. Muslim, A. H. Ruger, and K. Kusrini, “Sentimen Analisis Terhadap Komentar Konsumen Terhadap Produk
Penjualan Toko Online Menggunakan Metode K-Means,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa
Komputer, vol. 19, no. 2, pp. 207–213, 2020.
[10] M. Tika Adilah, H. Supendar, R. Ningsih, S. Muryani, and K. Solecha, “Sentiment Analysis of Online Transportation Service
Using The Na¨ıve Bayes Methods,” Journal of Physics: Conference Series, vol. 1641, no. 1, 2020.
[11] S. Wadhwa, “Performance Comparison of Classifiers on Twitter Sentimental Analysis,” pp. 50–61, 2022.
[12] S. P. Kristanto and L. Hakim, “Ekstraksi Informasi DestinasiWisata Populer Jawa Timur Menggunakan Depth-First Crawling,”
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 21, no. 1, pp. 229–236, 2021.
[13] B. J. Gogoi, “Changing Consumer Preferences: Factors Influencing Choice of Fast Food Outlet,” Academy of Marketing Studies
Journal, vol. 24, no. 1, pp. 1–17, 2020.
[14] M. Schmitt, S. Steinheber, K. Schreiber, and B. Roth, “Joint Aspect and Polarity Classification for Aspect-Based Sentiment
Analysis with End-to-End Neural Networks,” Proceedings of the 2018 Conference on Empirical Methods in Natural Language
Processing, EMNLP 2018, pp. 1109–1114, 2020.
[15] D. E. Kurniawati and R. Z. Khoirina, “Online-Based Transportation Business Competition Model of Gojek and Grab,” Advances
in Social Science, Education and Humanities Research, vol. 436, pp. 1054–1057, 2020.
[16] F. R. Azmi, H. Musa, S. H. M. Zailani, and S. F. Fam, “Analysis of Mitigation Strategy for Operational Supply Risk: An
Empirical Study of Halal Food Manufacturers in Malaysia,” Uncertain Supply Chain Management, vol. 9, no. 4, pp. 797–810,
How to Cite
Efendi, S., & Sihombing, P. (2022). Sentiment Analysis of Food Order Tweets to Find Out Demographic Customer Profile Using SVM. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 21(3), 583-594.