Go-Food Sentiment Analysis Using Twitter Data, Compared the Performance of the Random Forest Algorithm with That of the Linear Support Vector Classifier

DOI

10.2991/978-94-6463-084-8_2How to use a DOI?

Keywords

Association Rules; A priori Algorithm; Data Mining

Abstract

As a generalization, many modern consumers now favor using one of the many available e-commerce websites to do their shopping. Customers can save time and energy by shopping online instead of going out to physical stores because they can do so whenever they like, from wherever they like. Eighty percent of the dataset is used for training, while twenty percent is used for validation. With these default settings for the training data, the random forest algorithm is applied to the classification with 40 n estimators and linear SVC. Accuracy, precision, recall, and the F-measure are just a few of the quantitative metrics we employ to assess the quality of the model. Random forest has a 98.6% success rate, while linear SVC only achieves a success rate of 98%. Training data for a random forest can take up to 5 min, but training data for a linear SVC only takes 1 min. Sentiment analysis performed with machine learning’s random forest algorithm and linear SVC on Go-Food reviews in Indonesian found that positive sentiment was still higher than negative sentiment as of June 2022.

Copyright

© 2022 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Muhammad Abdullah Hadi
AU  - Nizirwan Anwar
AU  - Budi Tjahjono
AU  - Lina
AU  - Binastya Anggara Sekti
AU  - Yunita Fauzi Achmad
AU  - Yulhendri
PY  - 2022
DA  - 2022/12/26
TI  - Go-Food Sentiment Analysis Using Twitter Data, Compared the Performance of the Random Forest Algorithm with That of the Linear Support Vector Classifier
BT  - Proceedings of the First Mandalika International Multi-Conference on Science and Engineering 2022, MIMSE 2022 (Informatics and Computer Science) (MIMSE-I-C-2022)
PB  - Atlantis Press
SP  - 3
EP  - 13
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-084-8_2
DO  - 10.2991/978-94-6463-084-8_2
ID  - Hadi2022
ER  -