Feature Construction
Explore how to construct meaningful text features such as sentiment analysis scores, length-based metrics, text complexity, and linguistic attributes. Understand practical methods to enhance machine learning models by deriving new representations from raw text data, including named entity extraction using Python libraries like spaCy.
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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 positive, negative, and neutral sentiment scores to automatically assess customer reviews for product sentiment.
Length-based features: We can measure the length of text entries in terms of words, ...