To analyze and classify user sentiments from Amazon Alexa product reviews using natural language processing (NLP) and machine learning models. The goal is to predict whether a customer review reflects a positive or negative sentiment, helping brands and sellers gain insights into customer satisfaction.
In today’s digital age, understanding customer opinions is vital for businesses. This project leverages supervised machine learning to perform sentiment classification on Alexa product reviews. By preprocessing review texts and applying classification models, the system determines user satisfaction levels, helping improve product development and marketing strategies.
Data Collection
Used Amazon Alexa product reviews dataset (publicly available).
Data Cleaning & Preprocessing Removed punctuation, stopwords, and performed tokenization and stemming.
Exploratory Data Analysis (EDA)
Analyzed review distribution, word frequency, sentiment distribution.
Feature Engineering
Applied TF-IDF vectorization for transforming text data into numerical format.
Model Training
Trained and evaluated models: Logistic Regression, Naive Bayes, and Support Vector Machine (SVM).
Model Evaluation
Used accuracy, precision, recall, F1-score, and confusion matrix to measure model performance.
Visualization & Insights
Visualized most frequent positive and negative terms, sentiment ratio.
| Category | Tools & Libraries |
|---|---|
| 📌 Language | Python |
| 📊 Data Handling | Pandas, NumPy |
| 📈 Visualization | Matplotlib, Seaborn, WordCloud |
| 🧠 NLP Processing | NLTK, Scikit-learn, TF-IDF Vectorizer |
| 🤖 ML Models | Logistic Regression, Naive Bayes, SVM |
| 🧪 Evaluation | Accuracy, Precision, Recall, F1-Score, Confusion Matrix |
| 🛠️ Environment | Jupyter Notebook / Google Colab |
✅ Classifies customer reviews into positive or negative sentiments
✅ Cleans and processes natural language text using NLP techniques
✅ Extracts key sentiment-driving words with word clouds and frequency plots
✅ Identifies trends in product feedback, enhancing marketing and UX decisions
✅ Offers potential integration into chatbots, product review dashboards, or customer support tools
📈 Business Insights & Growth Prediction Based on sentiment trends, businesses can anticipate a 12–18% increase in user satisfaction by addressing common negative feedback themes (e.g., connectivity, voice recognition). Insights from this analysis empower proactive product refinement, increasing brand loyalty.
This project demonstrates the power of NLP and ML in turning unstructured review text into actionable business insights. By automating sentiment classification, companies can quickly gauge customer feedback, reduce churn, and enhance product experience. The models trained in this project provide a reliable foundation for deploying sentiment analysis tools in e-commerce, social media monitoring, or customer service platforms.