How Logistic Regression Works for Text Classification?

Logistic Regression is a statistical method used for binary classification problems, and it can also be extended to handle multi-class classification. When applied to text classification, the goal is to predict the category or class of a given text document based on its features.

Steps for how Logistic Regression works for text classification:

1. Text Representation:

• Before applying logistic regression, text data should be converted as numerical features known as text vectorization.
• Common techniques for text vectorization include Bag of Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), or more advanced methods like word embeddings (Word2Vec, GloVe) or deep learning-based embeddings (BERT, GPT).

2. Feature Extraction:

• Once data is represented numerically, these representations can be used as features for model.
• Features could be the counts of words in BoW, the weighted values in TF-IDF, or the numerical vectors in embeddings.

3. Logistic Regression Model:

• Logistic Regression models the relationship between the features and the probability of belonging to a particular class using the logistic function.
• The logistic function (also called the sigmoid function) maps any real-valued number into the range [0, 1], which is suitable for representing probabilities.
• The logistic regression model calculates a weighted sum of the input features and applies the logistic function to obtain the probability of belonging to the positive class.

Text classification is the process of automatically assigning labels or categories to pieces of text. This has tons of applications, like sorting emails into spam or not-spam, figuring out if a product review is positive or negative, or even identifying the topic of a news article. In this article, we will see How logistic regression is used for text classification with Scikit-Learn.