Summary
The strategies outlined provided a systematic approach to experimenting with different techniques for building and training sentiment analysis models using the Amazon product review dataset. Each step was meticulously detailed, with code generation and user interaction tailored to a beginner-friendly experience, ensuring a comprehensive learning journey.
The task began with constructing a baseline sentiment analysis model. Initial steps included preprocessing text data by tokenizing, lowercasing, and removing stopwords and punctuation, followed by feature engineering using TF-IDF encoding.
Detailed Python code was provided in a Jupyter Notebook format, complete with explanations for each operation, ensuring that even beginners could follow along comfortably.
We explored baseline machine learning models, starting with logistic regression. The model was trained and evaluated, revealing a significant class imbalance that skewed predictions toward the majority class...
