Analyzing an Interest in GPT 4o through Sentiment Analysis using CRISP-DM

Abstract

This study investigates the sentiment of viewers towards GPT-4o technology videos by analyzing 1538 English language posts using two sentiment analysis tools, VADER and TextBlob. The analysis reveals a fair level of agreement between the two tools, with 929 posts (60.40%) classified consistently, yielding a Cohen’s kappa statistic of 0.388. The sentiment distribution among the posts is as follows: 182 posts (19.59%) exhibit negative sentiments, 390 posts (41.98%) are neutral, and 357 posts (38.43%) show positive sentiments. These findings highlight the importance of utilizing multiple tools for comprehensive sentiment analysis and underscore the complexity of interpreting public reactions to AI advancements. The study provides valuable insights into the nuanced responses of viewers, emphasizing the diverse perspectives towards the GPT-4o technology.

Keywords

• sentiment
• classification
• vader
• textblob
• gpt-4o