https://github.com/tazeenrashid/amazon-reviews-nlp-project

Analyzed 500 product reviews using NLP and Sentiment Analysis especially Ranking Feature to derive actionable recommendations. Used Sentimental Analysis, TF IDF, Sentiment-Keyword Mapping, and Random Forrest to build a model to help make a ranking based features output based on what is important for customers.
https://github.com/tazeenrashid/amazon-reviews-nlp-project

amazon nlp-machine-learning random-forest-classifier sentiment-analysis sentiment-maps

Last synced: 2 days ago
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Analyzed 500 product reviews using NLP and Sentiment Analysis especially Ranking Feature to derive actionable recommendations. Used Sentimental Analysis, TF IDF, Sentiment-Keyword Mapping, and Random Forrest to build a model to help make a ranking based features output based on what is important for customers.

• Host: GitHub
• URL: https://github.com/tazeenrashid/amazon-reviews-nlp-project
• Owner: TazeenRashid
• Created: 2024-11-24T20:05:39.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-11-24T20:32:05.000Z (2 months ago)
• Last Synced: 2024-12-03T14:13:14.060Z (2 months ago)
• Topics: amazon, nlp-machine-learning, random-forest-classifier, sentiment-analysis, sentiment-maps
• Language: Jupyter Notebook
• Homepage:
• Size: 11.2 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Amazon-Product-Sentiment-Analysis-and-Features-Ranking

Problem Statement

In today's competitive e-commerce landscape, understanding customer sentiment is key to enhancing user experience and driving product improvement. With an overwhelming amount of customer reviews, analyzing feedback manually is not scalable. This project focuses on sentiment analysis of Amazon product reviews to extract actionable insights. Additionally, it ranks product features based on their impact on customer sentiment, enabling data-driven decision-making for product enhancement.

Objectives:

Sentiment Analysis:
 Classify customer reviews into positive, negative, or neutral sentiments using Natural Language Processing (NLP) techniques.

Feature Extraction and Ranking:
 Identify product features frequently mentioned in reviews and rank them based on their influence on sentiment.

Actionable Insights:
 Provide visualizations and reports to highlight areas of improvement and strengths of the product, aiding strategic planning.




Key Challenges:

Processing large volumes of unstructured text data.

Extracting meaningful product features and linking them with sentiment.

Designing a model capable of accurately classifying sentiment while maintaining efficiency.