Sentiment Analysis of Shopee App Reviews on Google Play Store Using Machine Learning Models
Dublin Core
Title
Sentiment Analysis of Shopee App Reviews on Google Play Store Using Machine Learning Models
Creator
Fanuel Oley, Jutono Gondohanindijo
Proceedings Item Type Metadata
meta_title
Sentiment Analysis of Shopee App Reviews on Google Play Store Using Machine Learning Models
Abstract/Description
This study aims to analyze user reviews of the Shopee application on the Google Play Store using machine learning models to classify sentiments into positive, neutral, and negative categories. Data collection was carried out by web scraping, resulting in 4,000 review samples. The preprocessing steps included cleaning, tokenization, stopword removal, stemming, and TF-IDF vectorization. Three classification algorithms were tested, namely Naive Bayes, Support Vector Machine (SVM), and Neural Network. Evaluation results showed that the Naive Bayes model achieved the highest accuracy at 81.2%, followed by SVM at 78.6%, and Neural Network at 75%. Furthermore, a sentiment analysis dashboard was developed using Gradio and deployed through Hugging Face to facilitate marketing decision-making based on user perceptions. This research proves the effectiveness of machine learning in sentiment analysis and offers actionable insights to enhance customer satisfaction.
publication_date
2025/06/15
pdf_url
https://insyma.org/proceedings/files/articles/Jutono_Gondohanindijo.pdf
abstract_html_url
https://insyma.org/proceedings/items/show/393
keywords
Sentiment Analysis, Shopee, Machine Learning, Naive Bayes, SVM, Neural Network
firstpage
505
lastpage
509
issn
3047-857X
conference
Proceedings of the International Symposium on Management (INSYMA)
Volume
22
publisher_name
Fakultas Bisnis dan Ekonomika, Universitas Surabaya
no article
82
Citation
Fanuel Oley, Jutono Gondohanindijo, “Sentiment Analysis of Shopee App Reviews on Google Play Store Using Machine Learning Models,” Proceedings of the International Symposium on Management (INSYMA), accessed July 1, 2025, https://insyma.org/proceedings/items/show/393.