Analisis Sentimen Terhadap Aplikasi Chatgpt Pada Twitter Menggunakan Algoritma Naïve Bayes - Rifaldi, Muhamad Ilmar; Ramadhan, Yudhi Raymond; Jaelani, Irsan (STIKOM Tunas Bangsa Pematangsiantar, 2023)
Sentiment analysis or opinion mining is the detection of attitudes, opinions and emotions towards an object. The value of sentiment analysis can be divided into 2 types of sentiment, namely positive and negative sentiment. One of the topics that is currently being discussed by the public on Twitter social media is an AI-based chatbot application, ChatGPT. This research makes public tweets on Twitter as sentiment analysis material. Data retrieval is done using the Twitter API with data processing on Rapidminer and visualization using Power BI. The sentiment analysis process uses the Naïve Bayes algorithm by dividing the data of 301 tweets into training data and test data. Then testing using Confusion matrix to calculate the performance of the classification results. From the test results, 80% accuracy was obtained, then the Precission value was 80.95% and the Recall value 89.47%. It can be concluded that public sentiment on Twitter for the ChatGPT application tends to be positive seen from the number of positive tweet data as much as 74%.