I’m happy to report that I’ve examined the Project 1 feedback and made the necessary adjustments. I have learnt the following

NLTK is a popular Python library for natural language processing that provides an extensive toolkit for tasks like tokenization and sentiment analysis. In a more straightforward manner, TextBlob appears as a user-friendly Python library that makes text processing easier. It includes tools designed specifically for manipulating textual data as well as a sentiment analysis API. Using a rule-based methodology and a pre-built lexicon, VADER, an acronym for Valence Aware Dictionary and Sentiment Reasoner, specializes in sentiment analysis for social media text. Especially for sentiment analysis projects, Scikit-learn is a well-known machine learning framework that is useful for building and evaluating machine learning models. Finally, pre-trained models like BERT and GPT are introduced via Hugging Face’s Transformers Library, enabling users to adjust them for particular sentiment analysis applications.