ChatGPT and Sentiment Analysis: Unveiling Emotional Insights in Textual Data

Sentiment analysis plays a critical role in understanding public opinion on social media platforms. This research article presents an in-depth analysis of sentiment in ChatGPT-generated tweets using Natural Language Processing (NLP) techniques which has implications for various domains, including market research, brand reputation management, and public opinion analysis. The objective is to improve the accuracy and effectiveness of sentiment analysis on ChatGPT-generated content. The study begins by pre-processing the tweet data, including the removal of punctuation, special characters, and user mentions. Tokenization is applied to convert the tweets into a structured format while eliminating stopwords to focus on meaningful words. Stemming and lemmatization techniques are employed to further enhance word normalization. Visualizations, such as word clouds, provide insights into the most frequently used words in the ChatGPT tweets, uncovering prevalent topics and themes within the dataset. Sentiment analysis is conducted using Text-Blob and nltk libraries, comparing nltk’s Vader Sentiment Intensity Analyzer outperforms TextBlob, achieving an average accuracy of 85% compared to 76%. A machine learning model is constructed using the LinearSVC algorithm, incorporating the Tfidf Vectorizer for feature extraction. The model achieves an accuracy of 89% in sentiment prediction when trained and evaluated on the tweet dataset. To validate the model’s performance, an equalized dataset is created, balancing the number of positive, neutral, and negative tweets. It demonstrates consistent accuracy across different sentiment classes, with average accuracies of 87% for positive sentiment, 92% for neutral sentiment, and 86% for negative sentiment.