---
title: "Building an End-to-End Sentiment Analysis Pipeline with Scikit-LLM"
date: 2026-06-16
source: https://machinelearningmastery.com/building-an-end-to-end-sentiment-analysis-pipeline-with-scikit-llm/
description: "Traditional machine learning pipelines for predictive tasks like text classification usually rely on extracting structured, numerical features from raw text — for instance, TF-IDF frequencies or token embeddings — to feed into classical models such as logistic regression, ensembles, or support vector machines."
---

# Building an End-to-End Sentiment Analysis Pipeline with Scikit-LLM

Traditional machine learning pipelines for predictive tasks like text classification usually rely on extracting structured, numerical features from raw text — for instance, TF-IDF frequencies or token embeddings — to feed into classical models such as logistic regression, ensembles, or support vector machines.

*Published: 2026-06-16*