https://github.com/selcia25/sentiment-analysis-googleplaystore-reviews

📲This project aims to analyze Google Play Store app reviews to extract insights regarding user sentiments towards various mobile applications.
https://github.com/selcia25/sentiment-analysis-googleplaystore-reviews

data-extraction data-preprocessing exploratory-data-analysis machine-learning matplotlib natural-language-processing python sentiment-analysis web-scraping

Last synced: 8 months ago
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📲This project aims to analyze Google Play Store app reviews to extract insights regarding user sentiments towards various mobile applications.

• Host: GitHub
• URL: https://github.com/selcia25/sentiment-analysis-googleplaystore-reviews
• Owner: selcia25
• License: mit
• Created: 2024-02-06T13:39:57.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-02-06T13:45:27.000Z (over 1 year ago)
• Last Synced: 2025-01-02T08:14:34.703Z (10 months ago)
• Topics: data-extraction, data-preprocessing, exploratory-data-analysis, machine-learning, matplotlib, natural-language-processing, python, sentiment-analysis, web-scraping
• Language: Jupyter Notebook
• Homepage:
• Size: 3.88 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Sentiment Analysis in Google Play Store App Reviews

## Project Overview

This project aims to analyze Google Play Store app reviews to extract insights regarding user sentiments towards various mobile applications. The analysis involves the application of natural language processing (NLP) and machine learning techniques to classify reviews into positive, negative, and neutral sentiments.

## Key Tasks and Technologies Used

- **Sentiment Analysis**

- **Exploratory Data Analysis (EDA)**

- **Data Extraction**

- **Data Processing**

- **Web Scraping**

- **Python**

- **Matplotlib**

## Project Description

The project involved collecting app reviews from the Google Play Store using web scraping techniques. Once the data was extracted, it underwent preprocessing and data processing steps to prepare it for analysis. Sentiment analysis models were developed using Python and NLP libraries to classify the reviews based on their sentiment polarity. Additionally, exploratory data analysis (EDA) was conducted to uncover patterns, trends, and key features impacting user sentiments.

## Project Outcome

The insights derived from the analysis provided valuable information for understanding user preferences and sentiments towards various mobile applications available on the Google Play Store. The project demonstrated the application of NLP and machine learning techniques in extracting actionable insights from user-generated content.