Sentiment Analysis
Sentiment Analysis on Product Reviews
Client
Self Development
YeAr
2025
Category
Natural Language Processing (NLP)
Service
Automation & Efficiency Gains
Tools / Languages Used
- Python for data analysis and model development
- Libraries: pandas, scikit-learn, nltk, matplotlib, seaborn
- Modeling Techniques: Bag-of-Words, TF-IDF, Logistic Regression, Naive Bayes
- Environment: Jupyter Notebook / VS Code
Technical Skills
- Text preprocessing (tokenization, stopword removal, lemmatization)
- Feature extraction using Bag-of-Words and TF-IDF vectorization
- Supervised machine learning (classification models)
- Model evaluation metrics: accuracy, precision, recall, F1-score, confusion matrix
- Data visualization for text distribution and word frequency analysis
- Pipeline building with scikit-learn
Soft Skills
- Analytical thinking: Defined clear success metrics and iteratively refined models.
- Problem-solving: Diagnosed class imbalance and improved model generalization.
- Communication: Interpreted results and translated technical findings into actionable insights (e.g., what words drive customer satisfaction).
- Storytelling: Presented results in a narrative linking model insights to business impact.
Step 1: Exploratory Data ANalysis
- Loaded and inspected dataset of product reviews (e.g., 50,000+ samples).
- Analyzed class balance between positive and negative reviews.
- Visualized most frequent words using word clouds and bar plots.
- Identified patterns:
- Positive reviews commonly used words like “love”, “great”, “amazing”.
- Negative reviews included “worst”, “broken”, “terrible”.
- Conducted text-length analysis to understand verbosity trends between sentiments.
Step 2: Solution Design
- Designed a supervised learning pipeline consisting of:
- Text preprocessing: lowercasing, removing punctuation, and tokenizing text.
- Feature extraction: converted text to numerical vectors using Bag-of-Words and TF-IDF.
- Model selection: experimented with Logistic Regression, Multinomial Naive Bayes, and Support Vector Machines.
- Used GridSearchCV for hyperparameter tuning.
- Split data into 80% training and 20% testing to ensure unbiased evaluation.
Step 3: Model Assessment
- Compared models using accuracy and F1-score:
- Logistic Regression: ~89% accuracy
- Naive Bayes: ~87% accuracy
- Evaluated confusion matrix to identify false positives and negatives.
- Interpreted model coefficients to identify influential words:
- Positive indicators: “excellent”, “love”, “fantastic”
- Negative indicators: “waste”, “poor”, “refund”
Step 4: Results / How It’s Used
- Final model achieved ~90% accuracy on unseen test data.
- Potential applications:
- Automating customer feedback monitoring
- Brand reputation tracking
- Enabling product teams to identify issues faster
- Final model achieved ~90% accuracy on unseen test data.
- Potential applications:
- Automating customer feedback monitoring
- Brand reputation tracking
- Enabling product teams to identify issues faster
