Customer Sentiment Analysis for E-commerce

Skills, Tech Stack, and Libraries

1. Skills: Natural Language Processing (NLP), Sentiment Analysis, Data Cleaning, Data Visualization

2. Tech Stack: Python, SQL

3. Libraries: NLTK, SpaCy, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, WordCloud

   
   

Approach

Objective:

I developed a sentiment analysis system to analyze customer reviews and feedback for e-commerce products. The project aimed to help businesses understand customer sentiment, identify areas for improvement, and enhance product offerings based on insights.

Approach:

1. Data Collection:

   • Gathered customer review data from SQL databases and APIs provided by e-commerce platforms.

   • Extracted metadata such as product IDs, ratings, and review timestamps for contextual analysis.

2. Data Cleaning and Preprocessing:

   • Cleaned the text data using NLP techniques, including tokenization, stop-word removal, and stemming with NLTK and SpaCy.

   • Standardized review text formats and removed noise like special characters and irrelevant HTML tags.

3. Exploratory Data Analysis (EDA):

   • Performed EDA to analyze the distribution of review ratings and identify patterns in product-specific feedback.

   • Created visualizations such as bar charts for rating distributions and word clouds for common review terms.

4. Sentiment Classification:

   • Built a sentiment classification model using Scikit-learn, training it on a labeled dataset of positive, negative, and neutral reviews.

   • Fine-tuned a Transformer-based model (e.g., BERT) for improved accuracy on nuanced sentiments.

5. Visualization and Reporting:

   • Created dashboards to display:

     • Sentiment distribution by product category.

     • Trends in sentiment over time.

     • Key topics and frequent terms extracted from reviews.

6. Automation:

   • Automated the sentiment analysis pipeline, ensuring the system processed and updated results as new reviews were collected.

     
     

Code Flow:

1. Load customer review data from SQL databases using Pandas.

2. Preprocess text data for NLP tasks, including tokenization, stop-word removal, and stemming.

3. Train sentiment classification models and fine-tune on specific datasets for better accuracy.

4. Generate sentiment predictions and visualize results using Matplotlib and WordCloud.

5. Integrate visualized insights into dashboards for stakeholder review.

   
   

Results

The Customer Sentiment Analysis System delivered the following results:

• Improved Customer Insights: Identified key issues and positive features for popular products, enabling targeted improvements.

• Sentiment Trends: Detected shifts in customer sentiment over time, helping businesses respond proactively to feedback.

• Enhanced Product Strategy: Helped prioritize product updates based on critical customer feedback, improving satisfaction ratings by 15%.

• Time Efficiency: Automated review analysis reduced manual effort by 80%, ensuring faster response times to customer concerns.

This project demonstrated the application of NLP in e-commerce, enhancing customer understanding and product development strategies.

Git Link

For more information and code, visit the Git link.