• More Akanksha Sopan

In the contemporary digital era, e-commerce platforms like Amazon have become major hubs for consumers to express their opinions and experiences through product reviews. These reviews offer valuable insights into customer sentiments and perceptions, making them a critical resource for businesses to understand their products' strengths and weaknesses. This research paper presents a comprehensive study on sentiment analysis applied to scraped Amazon product reviews, aiming to extract meaningful sentiment patterns and trends. The paper begins by introducing the significance of sentiment analysis in the context of e-commerce and its potential to guide business strategies and decision-making processes. Leveraging natural language processing (NLP) techniques, the study focuses on the sentiment polarity of product reviews, categorizing them into positive, negative, and neutral sentiments. A curated dataset of scraped Amazon product reviews is preprocessed and annotated for training machine learning models. The research employs a comparative analysis of various sentiment analysis methods, including rule-based approaches, traditional machine learning algorithms, and advanced deep learning models. These models are trained and evaluated for their effectiveness in accurately classifying sentiments present in the reviews. Furthermore, the study explores the impact of review length, language nuances, and product categories on sentiment classification performance. To validate the findings, the paper presents a case study involving a selection of popular product categories on Amazon. The sentiment analysis results are used to derive actionable insights for product managers, marketers, and business strategists. The paper also discusses potential limitations, such as sarcasm detection and context understanding, and suggests avenues for future research to address these challenges.

Sentiment analysis, web scraping, Amazon product reviews, natural language processing, machine learning, data collection, sentiment classification, customer feedback.

"Sentiment Analysis For Amazon Product Reviews", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 9, page no.b102-b107, September-2023, Available :http://www.jetir.org/papers/JETIR2309114.pdf

"Sentiment Analysis For Amazon Product Reviews", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 9, page no. ppb102-b107, September-2023, Available at : http://www.jetir.org/papers/JETIR2309114.pdf