Opinion Mining on Chat GPT based on Twitter Users

Abstract

The presence of Chatbots can assist humans in their everyday lives. Chat GPT is one of the commonly used Chatbots that humans rely on to support their work, serve as an assistant, or even create artistic works or writings. The purpose of this research is to investigate opinions regarding the presence of Chat GPT. This Opinion Mining method is conducted by crawling data from Twitter, which can be categorized into three opinions: Positive, Negative, or Neutral. To calculate the accuracy level of the model created, two algorithms, Naïve Bayes and K-Nearest Neighbour, are compared. The model validation process utilizes K-Fold Cross Validation by varying the value of k (k=2, k=4, k=6, k=8, and k=10) and different sampling methods, namely Linear, Shuffled, and Stratified, to obtain optimal accuracy values. The research results indicate that the K-Nearest Neighbour Algorithm achieves the highest accuracy value of 92.40%. Based on this comparison, the K-Nearest Neighbour Algorithm is deemed suitable for modeling Opinion Mining of Chat GPT. The distribution of Twitter users' opinion percentages regarding Chat GPT is as follows: Positive 9.4%, Negative 1.4%, and Neutral 89%. Neutral opinions dominate the results of the conducted Opinion Mining.Keyword : chat GPT, opinion mining, twitter