Natural Language Processing | Customer Insights | Text Analytics

Overview

This project analyzes Yelp customer reviews to extract actionable insights on customer sentiment, satisfaction, and business performance. Using Python NLP techniques, the project classifies reviews as positive, negative, or neutral and identifies recurring themes that can guide marketing, product, and service strategies.

Business Problem

Businesses often struggle to understand customer feedback because:

• Reviews are unstructured text

• Large volumes make manual analysis impossible

• Patterns in customer satisfaction and complaints are hidden

• Decision-makers lack clear summaries of customer sentiment

The goal was to create a repeatable framework to analyze, visualize, and interpret customer reviews at scale.

Project Objectives

1. Clean and preprocess raw review text for analysis

2. Apply NLP techniques to classify sentiment (positive, neutral, negative)

3. Identify key themes and topics within customer feedback

4. Create visualizations for stakeholders to quickly understand trends

5. Provide actionable recommendations for product and service improvement

Tools & Technologies

• Python

• Pandas & NumPy for data cleaning and manipulation

• NLTK / SpaCy for text preprocessing

• Scikit-learn for text classification

• TF-IDF Vectorization for feature extraction

• Matplotlib / Seaborn / WordCloud for visualization

• Jupyter Notebook for workflow presentation

Data Preparation

• Removed stop words, punctuation, and irrelevant text

• Tokenized text and normalized to lowercase

• Lemmatized words for consistency

• Converted reviews into TF-IDF feature vectors

• Split data into training and test sets

• Ensured balanced class distribution for model performance

Modeling Approach

• Logistic Regression for baseline sentiment classification

• Random Forest / Gradient Boosting for improved accuracy

• Naive Bayes for comparison with traditional NLP models

Evaluation metrics included:

• Accuracy

• Precision, Recall, F1-Score

• Confusion Matrix

Dashboard & Visualizations

• Word Clouds: Highlight most common words in positive vs negative reviews

• Sentiment Distribution Charts: Show proportion of positive, neutral, negative reviews

• Topic Modeling (optional): Identify recurring themes in customer feedback

• Trend Analysis: Sentiment changes over time or by location

Business Impact

• Enabled leadership to prioritize customer pain points

• Guided marketing campaigns toward strengths and addressed weaknesses

• Reduced manual review analysis time by 80%

• Provided a repeatable framework for ongoing sentiment monitoring

Deliverables

• Cleaned and preprocessed review dataset

• Python scripts for NLP preprocessing, modeling, and visualization

• Visual dashboards for sentiment and topic analysis

• Executive insights summary for business action

Project Links

• View Notebook (Insert GitHub link)

• View Dashboard / Visuals (Optional: link or screenshot)

Let’s Work Together

I help businesses transform unstructured customer feedback into actionable insights using NLP and data analytics.