Customer Review Sentiment Analysis

This project performs sentiment analysis on customer reviews from Cafe Coffee Day (CCD) using Natural Language Processing (NLP) techniques. The analysis includes data cleaning, sentiment classification, keyword extraction, and visualization of results.

Table of Contents

1. Project Overview
2. Technologies Used
3. Dataset
4. Features
5. Results
6. Business Interpretation

Project Overview

The goal of this project is to analyze customer sentiments expressed in reviews of Cafe Coffee Day, focusing on identifying key positive and negative sentiments, as well as visualizing the results through various plots.

Technologies Used

• Python
• Pandas
• NumPy
• NLTK
• TextBlob
• Scikit-learn
• Matplotlib
• Seaborn
• WordCloud
• Squarify
• Neattext
• Spacy

Dataset

The dataset used for this project is the CCDFullDataset.xlsx, which contains customer reviews of Cafe Coffee Day. Ensure that the dataset is present in the specified path in the code.

Features

• Text cleaning and preprocessing
• Sentiment classification (positive/negative)
• Keyword extraction and frequency analysis
• Visualization of sentiment distribution
• Confusion matrix and classification report for model evaluation
• Word clouds for positive and negative sentiments
• Lollipop and heatmap plots to visualize word frequency

Results

The analysis will produce various visualizations, including:

• Sentiment distribution pie chart
• Confusion matrix heatmap
• Word clouds for positive and negative sentiments
• Lollipop and heatmap plots for word frequency

Business Interpretation

- Objective:

The primary goal of this project is to analyze customer sentiment from CCD's reviews to gain insights into customer perceptions, preferences, and pain points. This analysis aims to inform strategic decision-making in marketing, product development, and customer service.

- Key Insights:

1. Customer Satisfaction: By analyzing the sentiment of reviews, the project identifies overall customer satisfaction levels. A high proportion of positive sentiments indicates strong customer loyalty, while negative sentiments highlight areas for improvement.
2. Product Performance: The analysis can reveal how specific products (e.g., drinks, snacks) are perceived. Understanding which items receive positive feedback can help CCD promote popular products, while identifying poorly rated items can guide product modifications or discontinuation.
3. Service Quality: Sentiment analysis can shed light on customer experiences regarding service quality, including speed, staff behavior, and ambiance. This insight allows CCD to implement targeted training programs and improve service delivery.

- Strategic Recommendations:

1. Enhance Customer Engagement: Based on positive sentiment themes, CCD can develop targeted marketing campaigns that emphasize the strengths of its products and services, engaging customers more effectively.
2. Address Negative Feedback: CCD should prioritize addressing common pain points identified in negative reviews. This could involve product improvements, staff training, or enhancing the overall customer experience.
3. Leverage Positive Reviews: Highlighting customer testimonials and positive reviews in marketing materials can enhance brand image and attract new customers.
4. Continuous Monitoring: Implementing a system for ongoing sentiment analysis will allow CCD to stay updated on customer perceptions and respond proactively to emerging trends or issues.

- Conclusion:

The sentiment analysis project serves as a powerful tool for CCD to understand its customers better and make informed decisions that align with their preferences and expectations. By leveraging data-driven insights, CCD can enhance customer satisfaction, improve product offerings, and ultimately drive business growth.