Feature Construction

Introduction

Feature construction is an essential technique in text preprocessing that involves creating new features or representations of text data to improve the performance of a machine-learning model. More specifically, this process involves combining or transforming existing features to capture important information or patterns in the data. For example, if we’re working with a review dataset, we might want to create a new review_length feature that contains the count of characters within the review column in a dataset. We can then use such a new feature as part of the training data to enhance the performance of a machine-learning model.

New feature categories

Here are a few categories of features that we can create when performing feature construction:

• Sentiment analysis features: We can develop sentiment-based scores that capture positive, negative, and neutral sentiments, as well as the polarity differences in the text data. For example, we can generate features like ...