Building an End-to-End Sentiment Analysis Pipeline

A tutorial has been published on how to construct an end-to-end sentiment analysis pipeline utilizing Scikit-LLM and open-source large language models accessed through the Groq API. This article explains the shift in traditional machine learning approaches, which typically rely on extracting structured features from raw text, to a methodology that leverages zero-shot or few-shot reasoning using pre-trained models for language tasks.

Scikit-LLM is introduced as a Python library that connects classical machine learning with modern LLM API calls. The article outlines the process of setting up Scikit-LLM with appropriate API credentials by connecting to an LLM API repository, specifically Groq. Users are guided to register on Groq and obtain an API key, which is essential for integrating the library with the Groq backend models.

The tutorial describes the dataset used, namely the IMDB movie reviews dataset, which contains approximately 50,000 instances of text reviews labeled with sentiments. It mentions the initial step of fetching a subset of 500 rows for demonstration purposes, emphasizing user flexibility to adjust this sample size.

Building the sentiment analysis pipeline involves a series of preprocessing, cleaning, and data preparation steps, followed by model setup and evaluation. The article explains how to create a preprocessing object and integrate it with a model instance to form a comprehensive pipeline for both training and inference stages. It highlights that no actual training occurs since a pre-trained model is utilized for classification.

Finally, the tutorial showcases the pipeline's performance in classifying sentiments in reviews, affirming its effectiveness in this application.