ChatGPT, a Large Language Model (LLM) concept, that enables human-machine interaction in natural conversation. ChatGPT has elicited diverse assumptions among its users, encompassing both positive and negative sentiments. Sentiment analysis reveals user opinions about ChatGPT, showing positives, negatives, and areas to improve. To achieve competent analysis results with minimal bias and diverse perspectives, this research leverages Artificial Neural Network (ANN) and Support Vector Machine (SVM). K-Nearest Neighbor (KNN) becomes the baseline model for ANN and SVM to reference. This research also evaluates the comparison of Word2Vec dimensions applied to each classification method. The results of this research show that the best combination is obtained using a 300-dimensional model on Word2Vec and using the ANN classification model. This is evidenced by an accuracy value of 87.45%, f1-score 87.45%, recall 87.45%, and precision 87.45%. This facilitates sentiment analysis with reduced bias and diverse perspectives, contributing to the enhancement of ChatGPT’s performance.