This project delved into the sentiment analysis using Python-based algorithms/models. The implementation phase encompassed crucial steps such as EDA and text preprocessing to ensure data readiness for analysis. Subsequently, the project proceeded to showcase the implementation and outcomes of both the VADER and RoBERTa models, shedding light on their respective sentiment distributions and classifications. The evaluation of the RoBERTa model provided insights into its performance, and further enhancements were made through fine-tuning. Additionally, the chapter explored the implementation of the TextCNN model, which exhibited an impressive accuracy rate. Overall, this project contributed valuable insights into the implementation and evaluation of Python-based algorithms and models for sentiment analysis, enriching our understanding of sentiment analysis techniques and their practical applications.