Text Classification
Learn about text classification and how to do it using Python.
We'll cover the following...
Introduction
When we build machine-learning models, we use the text classification technique as a first step to overcome the lack of labels, especially when we have two datasets: one dataset with labels and the other without.
In the context of text classification, a label is a categorical variable that we want to predict.
This process involves creating a new label for the second dataset using an existing text classification model. Therefore, we define text classification as a technique that classifies text content into predefined groups or categories. Here’s a table of a few commonly used classifiers for text classification and a brief description of when to use each:
Text Classification Classifiers
Classifier Name | When We Use It | Python Implementation Class |
Naive Bayes | When we have a small text dataset and want a simple baseline |
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Logistic regression | When we need a fast and interpretable classifier |
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Support vector machines | When we have a high-dimensional text dataset |
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Random forest | When we have a dataset with lots of outliers and noisy data |
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Gradient boosting | When we need high accuracy and can handle longer training times |
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Application
The following code example showcases creating a new label for an unlabeled dataset ...