SENTIMENT ANALYSIS OF CHATGPT APP USER REVIEWS USING SVM AND CNN METHODS - Widaad, Nurul; Anggraini, Dina (Informatika, Universitas Jenderal Soedirman, 2024)
The rapid development of Artificial Intelligence (AI) has significantly impacted various sectors, including user interactions with natural language-based applications such as ChatGPT. This study aims to analyze user sentiment towards ChatGPT amidst the emergence of alternative AI technologies like Gemini (Google Deep Mind), Claude (Anthropic AI), and LLaMA (Meta AI). ChatGPT was chosen as the research subject due to its role as a pioneer in public AI usage. The research focuses on uncovering user sentiments—positive, negative, or neutral. A total of 155,529 reviews from the Google Play Store were analyzed using Support Vector Machine (SVM) and Convolutional Neural Network (CNN) algorithms. The research process involved data collection (data scraping), preprocessing (emoji removal, case folding, punctuation removal, tokenization, stopword removal, stemming, and normalization), sentiment labeling, data splitting (80% training and 20% testing), and model evaluation using accuracy, precision, recall, and F1-score metrics. The results indicate that the SVM model achieved an accuracy of 85%, with a precision of 0.83, recall of 0.55, and an F1-score of 0.58. Meanwhile, the CNN model recorded an accuracy of 84%, with a precision of 0.68, recall of 0.59, and an F1-score of 0.62. Among the analyzed reviews, 75% expressed positive sentiment, 18.22% negative, and 6.71% neutral. The dominance of positive sentiment reaffirms ChatGPT's position as a preferred choice among users, although certain aspects need improvement to maintain its competitiveness amidst growing AI competition. This study provides valuable insights for developers to identify the strengths and weaknesses of ChatGPT based on user feedback, enabling them to optimize the application's features to create a more satisfying and relevant user experience in the future.