Google KSA Reviews: Comprehensive Analysis

Overview

This repository contains a comprehensive analysis of Google reviews for businesses and tourist destinations in Saudi Arabia. The primary objective of this project is to extract actionable insights from customer feedback to help businesses enhance their service quality, boost customer satisfaction, and maintain a competitive edge.

Project Structure

ksa-reviews-analysis/
├── data/
│ ├── dataset.csv
│ ├── mappings.json
│ ├── dataset_processed.csv
│ ├── dataset_translated.csv
│ ├── dataset_translated_with_sentiment_multi_themes.csv
│ ├── encoded_tags.csv
│ ├── tags.csv
├── graphs/
│ ├── ...
├── Google_Reviews_Analysis.ipynb
├── Google_Reviews_Analysis.html
├── README.md

Original Data Files

• data/dataset.csv: The original dataset containing reviews and meta data.
• data/mappings.json: The original hash-to-tags mappings file.

Processed Data Files

• data/dataset_translated.csv: The dataset after translation.
• data/dataset_translated_with_sentiment_multi_themes.csv: The dataset with added sentiment and theme classifications.
• data/tags.csv: The expanded tags into individual columns.
• data/encoded_tags.csv: The encoded tags for categories and cities.
• data/dataset_processed.csv: The final processed dataset after cleaning, preprocessing, analysis, etc.

Analysis Notebook

• Google_Reviews_Analysis.ipynb: The Jupyter notebook containing the full analysis, including data cleaning, sentiment analysis, theme extraction, and visualization.
• Google_Reviews_Analysis.html: An HTML report generated from the Jupyter notebook for easier viewing without needing a Jupyter environment.

Extracted Graphs and Plots

• graphs/: This directory contains all the graphs and plots generated during the analysis. These visualizations help in understanding trends, patterns, and significant insights derived from the data.

Analysis Overview

The analysis consists of the following key sections:

1. Introduction:

   • Importance of online reviews.
   • Objective and scope of the analysis.

2. Data Cleaning and Preprocessing:

   • Handling inconsistencies and missing values.
   • Structuring and standardizing the review data.

3. Sentiment Analysis:

   • Classifying reviews into positive, neutral, and negative sentiments using NLP techniques.
   • Understanding overall customer satisfaction trends.

4. Theme Extraction:

   • Identifying key themes and topics within the reviews.
   • Pinpointing common areas of feedback such as product quality, customer service, delivery, pricing, and user experience.

5. Visualization and Insights:

   • Creating visualizations to highlight trends, patterns, and significant insights.
   • Analyzing the relationship between sentiment, ratings, themes, categories, and cities.

6. Recommendations:

   • Providing actionable recommendations for businesses to improve their offerings based on customer feedback.
   • Strategies for enhancing product quality, customer service, delivery processes, pricing strategies, and user experience.

7. Conclusion:

   • Summarizing the key findings of the analysis.
   • Discussing the impact of the insights on business improvements and customer satisfaction.

Contributing

Contributions are welcome! If you have any suggestions or improvements, please create a pull request or open an issue.

License

This project is licensed under the Apache-2.0 License - see the LICENSE file for details.