🛍️ Online Retail Order Reviews NLP Project

This project performs Natural Language Processing (NLP) on a large dataset of online retail product reviews written in Portuguese. The goal is to analyze customer sentiments through review scores and text mining techniques like unigrams, bigrams, and trigrams.

It also visualizes rating distributions and frequent word patterns to better understand customer satisfaction and feedback trends.

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📘 Overview

This dataset contains over 100,000 product reviews from an online retail platform. The analysis focuses on:

• Understanding review score distribution (1–5 stars)
• Cleaning and normalizing Portuguese text
• Tokenizing text and removing stopwords
• Generating and analyzing N-grams (Unigrams, Bigrams, Trigrams)
• Visualizing frequent patterns for positive (5-star) and negative (1-star) reviews

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🧠 Key Insights

• 📊 5-star reviews dominate the dataset, indicating overall customer satisfaction.
• ⚠️ 1-star reviews show recurring issues related to delivery delays and product quality.
• 🔠 Top unigrams and bigrams reveal common terms customers use in both positive and negative feedback.
• 🗣️ Portuguese-specific stopwords were removed to retain only meaningful words.
• 🎨 Visualizations provide comparative frequency distributions for positive and negative review language.

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⚙️ Tools & Technologies

Tool	Purpose
Python	Core programming language
NLTK (Natural Language Toolkit)	Tokenization, stopword removal, and N-gram generation
Pandas	Data manipulation and handling
Matplotlib & Seaborn	Data visualization
UnicodeData	Removing text accents for normalization
Portuguese Stopwords	Filtering non-informative words from reviews

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📊 Project Workflow

🔹 1. Data Loading & Preprocessing

• Load dataset: order_reviews.csv (≈100,000 rows)
• Convert review_creation_date to datetime format
• Visualize review score distribution using pie charts and bar plots

🔹 2. Text Cleaning

• Convert all text to lowercase
• Remove accents using unicodedata.normalize()
• Tokenize text using nltk.word_tokenize()
• Remove Portuguese stopwords using NLTK’s corpus

🔹 3. N-Gram Generation

Generate:

• Unigrams → single words
• Bigrams → two-word combinations
• Trigrams → three-word combinations

Used for both:

• 5-star reviews (positive sentiment)
• 1-star reviews (negative sentiment)

🔹 4. Frequency Distribution Visualization

Plotted top 25 most frequent n-grams for both positive and negative reviews using:

nltk.FreqDist(tokens).plot(25, cumulative=False, color=color)

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📂 Project Structure

Online_Retail_Order_Reviews/
│
├── order_reviews_analysis.py         # Main Python analysis script
├── order_reviews.csv                 # Dataset (100k+ Portuguese reviews)
├── README.md                         # Project documentation
└── images/                           # (Optional) visualizations and n-gram plots

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🚀 How to Run

🔹 Prerequisites

Install all dependencies:

pip install pandas matplotlib seaborn nltk

Also download NLTK datasets (only once):

import nltk
nltk.download('punkt')
nltk.download('stopwords')

🔹 Execute the Script

python order_reviews_analysis.py

• Pie charts and bar plots display review distributions
• N-gram frequency charts visualize word usage patterns

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📈 Visualizations

1. Pie Chart: Distribution of review scores (1–5)

2. Bar Graph: Count of reviews per score on a dark grid background

3. N-gram Frequency Graphs:

   • Top 25 Unigrams for 5-star reviews
   • Top 25 Bigrams for 1-star reviews
   • Top 25 Trigrams for both categories

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💡 Future Enhancements

• Add sentiment polarity analysis using TextBlob or Vader
• Translate Portuguese text for global reporting
• Implement topic modeling (LDA) for deeper insight into customer issues
• Deploy a Streamlit NLP dashboard for interactive visualization
• Integrate word clouds for better visual storytelling

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👤 Author

Rachakonda Ganesh 📧 [rachakondaganesh60@gmail.com] 🔗 GitHub 🔗 LinkedIn

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🏁 Conclusion

This project highlights the power of NLP in retail analytics, converting unstructured text data into meaningful insights. By combining Python, NLTK, and visualization tools, it offers a data-driven view of customer satisfaction and recurring pain points — essential for enhancing user experience and improving product quality.