/Product_Sentiment_Analysis

This project employs NLTK, Prowebscraper, and Python for sentiment analysis on online product reviews. Through web scraping, EDA, and NLP, it evaluates user satisfaction by comparing actual ratings and sentiment scores

Primary LanguageJupyter Notebook

Sentiment Analysis on Online Product Reviews

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Overview

  • This project aims to gauge user satisfaction regarding a particular product by analyzing online product reviews. Utilizing tools such as NLTK, Prowebscraper, Python, Seaborn, Plotly, and Matplotlib, the project involves web scraping, exploratory data analysis (EDA), and natural language processing (NLP) to convert data into sentiment scores.

  • The ultimate goal is to compare the actual ratings with the analysis compound score and assess the project's success in accurately reflecting user sentiments4

Main Features

Web Scraping

  • Utilized Prowebscraper to extract online product reviews, ensuring a comprehensive dataset for analysis.

Exploratory Data Analysis (EDA)

  • Employed Seaborn, Plotly, and Matplotlib to visualize and understand patterns, trends, and insights within the review dataset.

Natural Language Processing (NLP)

  • Leveraged NLTK for sentiment analysis, converting textual data into sentiment scores using sentiment intensity analyzers.

Rating Comparison

  • Compared the actual ratings from the reviews with the sentiment analysis compound scores to evaluate the alignment between user sentiments and numerical ratings

Process Steps

Web Scraping

  • Used Prowebscraper to extract online product reviews.
  • Ensured comprehensive data collection for a thorough sentiment analysis.

Exploratory Data Analysis (EDA)

  • Employed Seaborn, Plotly, and Matplotlib for visualizing and exploring patterns within the review dataset.
  • Gained insights into user sentiments, key features, and potential correlations and shown various comparision using barplots.

Natural Language Processing (NLP)

  • Utilized NLTK for sentiment analysis.
  • Applied sentiment intensity analyzers to convert textual data into sentiment scores.

Rating Comparison

  • Aligned the sentiment analysis compound scores with actual ratings.
  • Assessed the project's success in accurately reflecting user satisfaction

Technical Skills

  • Programming Language: Python
  • Web Scraping: Prowebscraper
  • Data Visualization: Seaborn, Plotly, Matplotlib
  • Natural Language Processing (NLP): NLTK

Tools Covered

  • NLTK: Used for natural language processing and sentiment analysis.
  • Prowebscraper: Employed for web scraping to gather a comprehensive dataset of online product reviews.
  • Python: Primary programming language for this project.
  • Seaborn, Plotly, Matplotlib: Utilized for exploratory data analysis and data visualization.

Result

  • The project successfully achieved its goal of analyzing user sentiments about a particular product based on online reviews.
  • The comparison between actual ratings and sentiment analysis compound scores provides valuable insights into user satisfaction