Hands-On Transfer Learning with Python

Feature engineering

Once we have our textual data properly processed via the methods mentioned in the previous section, we can utilize some of the following techniques for feature extraction and transformation into numerical form. Code snippets to better understand feature engineering for textual data are available in the Jupyter Notebook feature_engineering_text_data.ipynb:

• Bag-of-words model: This is by far the simplest vectorization technique for textual data. In this technique, each document is represented as a vector on N dimensions, where N indicates all possible words across the preprocessed corpus, and each component of the vector either denotes the presence of the word or its frequency.
• TF-IDF model: The bag-of-words model works ...