Sentiment Analysis Project using Machine Learning and NLP in Python : Text classification pipeline to predict sentiment from customer reviews. Transform raw text into meaningful features, train multiple machine learning models, and evaluate their performance.
🧠 Steps
-Text preprocessing: remove stopwords, strip punctuation, apply lemmatization
-Feature extraction: represent text using Bag of Words (BoW) and TF-IDF vectors
-Model building: train and compare Logistic Regression, Naive Bayes, Random Forest, and XGBoost
-Model evaluation: measure Accuracy, Precision, Recall, F1-Score, and analyze Confusion Matrix results
-Visualization: explore sentiment distribution and generate word clouds for insights
-Deployment: package and serve your sentiment analysis model for practical use cases
🛠️ Tools & Libraries
- Programming Language: Python
- Data Handling: Pandas, NumPy
- Visualization: Matplotlib, Seaborn, WordCloud
- Machine Learning & NLP: Scikit-learn, NLTK, XGBoost
💼 Project Type
- Machine Learning
- Natural Language Processing (NLP)
- Sentiment Analysis
- Text Classification
- Data Science
- Python