Breaking the problem down into features
Given the Amazon product review dataset and the application of machine learning models for sentiment analysis, we will outline the following features to guide users through building and optimizing models for sentiment classification:
- Data preprocessing and feature engineering: Users will start by preprocessing the text data, including tasks such as tokenization, lowercasing, and removing stop words and punctuation. Additionally, feature engineering techniques such as Term Frequency-Inverse Document Frequency (TF-IDF) encoding or word embeddings will be applied to represent the text data in a format suitable for machine learning models.
- Model selection and baseline training: Users will select baseline machine learning models such as logistic regression, Naive Bayes, or support vector machines (SVMs) for sentiment classification. The selected model will be trained on the preprocessed data to establish a baseline performance for...
