Building a text classifier involves multiple stages: data preparation, feature extraction, model selection, training, and evaluation. The process begins with collecting labeled data relevant to the classification task. For example, sentiment analysis requires text labeled as "positive," "negative," or "neutral." Preprocessing the text data follows, which involves steps like cleaning, tokenization, stop-word removal, and lemmatization. These steps ensure the data is uniform and noise-free.
Next, feature extraction transforms the text into numerical representations suitable for machine learning models. Techniques like Bag of Words (BoW), TF-IDF, or embeddings (e.g., Word2Vec or BERT) are commonly used. Once features are extracted, a suitable classification algorithm is chosen based on the task complexity and dataset size. Traditional classifiers like Naïve Bayes or Support Vector Machines (SVMs) work well for simpler tasks, while deep learning models like CNNs, RNNs, or transformer-based architectures like BERT are ideal for more complex problems.
The model is then trained on the prepared data and validated using a separate validation set to optimize hyperparameters. After training, the classifier is evaluated using metrics like accuracy, precision, recall, and F1 score to assess its performance. Libraries like Scikit-learn, Hugging Face Transformers, and TensorFlow simplify the implementation and evaluation process. Finally, the classifier is deployed for real-world use in applications such as spam detection, sentiment analysis, or topic classification.