Enhancing Sentiment Analysis with Logistic Regression and NLP: A Case Study of 2023 Amazon Software Reviews

Sentiment analysis has emerged as an indispensable resource for gleaning invaluable insights from the copious amounts of textual data produced by customer reviews, social media, and feedback forms. This research proposes a novel methodology for sentiment analysis that integrates sophisticated natural language processing (NLP) strategies with cutting-edge machine learning models to enhance the precision of sentiment categorization. The proposed methodology addresses the limitations of traditional approaches by leveraging logistic regression and Term Frequency-Inverse Document Frequency (TF -IDF) feature extraction to categorize textual sentiment into positive, negative, or neutral categories. The model is trained and evaluated using the 2023 Amazon Product Dataset, specifically focusing on software reviews. The study compares the performance of the proposed model with established sentiment analysis tools like VADER and TextBlob. Additionally, the research highlights the ethical considerations and potential biases associated with sentiment analysis in product reviews and customer feedback. The findings emphasize the importance of developing context-aware and domain-specific sentiment analysis models while addressing privacy concerns and establishing ethical guidelines for responsible implementation. This study contributes to the growing field of sentiment analysis by presenting a robust and accurate methodology that can be applied across various industries to gain a deeper understanding of customer opinions and inform decision-making processes.