Welcome to this tutorial on text classification! In this guide, we will delve into the basics of text classification and explore various techniques to categorize text data.

What is Text Classification?

Text classification is a task of assigning categories to text documents. It is widely used in various applications, such as sentiment analysis, spam detection, and topic classification.

Common Techniques

  1. Bag of Words (BoW): This method represents text data as a vector of word frequencies.
  2. TF-IDF (Term Frequency-Inverse Document Frequency): It is an extension of the BoW method that considers the importance of words in a document.
  3. Word Embeddings: Techniques like Word2Vec and GloVe convert words into dense vectors that capture semantic meaning.

Example

Suppose we have a dataset of customer reviews. We want to classify these reviews into positive, negative, or neutral categories.

Data Preparation

  1. Text Cleaning: Remove unnecessary characters, punctuation, and stop words.
  2. Vectorization: Convert the cleaned text into numerical vectors using the chosen technique.

Model Training

  1. Choose a Model: Use a classification algorithm like Naive Bayes, Support Vector Machine, or Neural Networks.
  2. Train the Model: Fit the model to the training data.

Evaluation

  1. Test the Model: Evaluate the model's performance on the test data.
  2. Improve the Model: Tune hyperparameters and try different techniques to improve accuracy.

Further Reading

To learn more about text classification, we recommend the following resources:

Text Classification