Sentiment Analysis of User Reviews on Cryptocurrency Trading Platforms Using Pre-Trained Language Models for Evaluating User Satisfaction

The study examines user sentiment on the Indodax cryptocurrency trading platform using pre-trained Indonesian language models for sentiment analysis. A dataset of 25,000 user reviews was analyzed, revealing that most reviews expressed neutral sentiment, with positive sentiments accounting for 20% and negative sentiments under 4%. The sentiment classification models used include Support Vector Machine (SVM), Logistic Regression, and Naive Bayes. SVM achieved the highest predictive accuracy at 94.22%, followed by Logistic Regression at 93.62%. These models classified sentiments based on TF-IDF feature extraction, highlighting SVM's effectiveness in sentiment classification within the user reviews. Additionally, sentiment trends over time were analyzed, showing fluctuations in user satisfaction corresponding with market events and platform changes, emphasizing the importance of maintaining platform stability during high volatility. The study’s findings suggest actionable improvements for Indodax, such as addressing user concerns that lead to negative sentiments, like customer service and technical issues, while reinforcing platform strengths, such as ease of use. These insights enable Indodax to enhance user satisfaction and retention by monitoring sentiment trends and adjusting features accordingly. However, the study faces limitations due to the use of pre-trained models that may not fully capture Indonesian language nuances and the absence of demographic data, which limits the analysis to general sentiment trends. Future research could incorporate demographic insights and user behavior metrics to offer a more personalized understanding of user sentiment, ultimately aiding Indodax in delivering a more tailored and satisfying user experience.