Natural Language Processing (NLP) allows machines to understand and interpret human language. Scikit-learn provides powerful tools for handling basic NLP tasks like text classification, vectorization, and sentiment analysis.
Today, you'll explore how to clean text, convert it into numerical representations using techniques like Bag-of-Words and TF-IDF, and build classification models on text data using Scikit-learn.
- Text preprocessing: tokenization, stopwords, stemming
- Bag-of-Words & TF-IDF Vectorization
- Using <code>CountVectorizer</code> and <code>TfidfVectorizer</code>
- Naive Bayes and Logistic Regression for text classification
- Evaluating NLP models with accuracy, precision, recall
Exercise: Spam Detection with NLP
- Load a dataset like the SMS Spam Collection dataset from UCI or Kaggle.
- Preprocess the text: lowercase, remove punctuation, stopwords, and apply stemming.
- Use
CountVectorizerandTfidfVectorizerto convert text into features. - Train a
MultinomialNBandLogisticRegressionmodel. - Evaluate with a confusion matrix and classification report.
- Compare the effect of BoW vs TF-IDF on model performance.
Kaggle NLP Course
Main resource for today
Scikit-learn Text Feature Extraction
Official documentation for text feature extraction in Scikit-learn
Text Classification with Scikit-learn
Tutorial on building and evaluating text classifiers
SMS Spam Classifier Notebook
Example notebook for spam detection using Scikit-learn
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