Fine-Tuned LLMs for Sentiment Prediction — How to Analyze and Evaluate | by Pranay Dave | Aug, 2023

Evaluation of models on Hugging Face for sentiment prediction

Photo by Oleksandr Baiev on Unsplash

Sentiment analysis is an area that has witnessed a remarkable transformation in the era of large language models (LLMs). As the LLMs can understand the context of the text, they are proving to be a very powerful way to analyze sentiments. The number of LLMs that are available for sentiment analysis on Hugging Face is impressive. The last time I checked, when writing this story, the number of models on Hugging Face for the sentiment task was 3017! This is a considerable number. Gone are the days when sentiment analysis was done with a handful of techniques such as traditional machine learning with TFIDF features, counting positive and negative words, or with libraries such as VADER.

Though the huge number of models available is exciting, it can also be overwhelming. So this article will help you navigate the LLM jungle for sentiment analysis. I will take top models and show you how to analyze and evaluate them. This can help you better understand which model suits your sentiment analysis needs.

Sentiment analysis is a very important business KPI. Many enterprises take important decisions such as product promotion or discontinuation based on sentiment analysis of customer reviews.

Most of the fine-tuned models on Hugging Face already provide analysis and evaluation. So you may ask why you need to analyze and make your own evaluation. There are multiple reasons:

  • The evaluation provided by model developers is based on their data, which may not reflect your business.
  • Not all models may be suitable to your business use case, even if all are called sentiment analysis models.
  • The strategic importance of sentiment analysis demands analyzing and evaluating based on your specific business data.

The approach which I will take in this story is shown here. I will first select a few candidate models followed by establishing an evaluation criterion. All models will be used…

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