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Navigating Sentiment Analysis with Machine Learning
This chapter explores the complexities of training machine learning models for sentiment analysis, contrasting the efficiency of lexicon-based methods with the more nuanced approaches of large language models (LLMs). It discusses techniques like fine-tuning and zero-shot classification to enhance model performance while addressing their limitations. The chapter also introduces practical applications of platforms like Hugging Face Hub, providing insights into leveraging pre-trained models for effective sentiment analysis.
What are the current approaches for analyzing emotions within a piece of text? Which tools and Python packages should you use for sentiment analysis? This week, Jodie Burchell, developer advocate for data science at JetBrains, returns to the show to discuss modern sentiment analysis in Python.
Jodie holds a PhD in clinical psychology. We discuss how her interest in studying emotions has continued throughout her career.
In this episode, Jodie covers three ways to approach sentiment analysis. We start by discussing traditional lexicon-based and machine-learning approaches. Then, we dive into how specific types of LLMs can be used for the task. We also share multiple resources so you can continue to explore sentiment analysis on your own.
This week’s episode is brought to you by Sentry.
Course Spotlight: Learn Text Classification With Python and Keras
In this course, you’ll learn about Python text classification with Keras, working your way from a bag-of-words model with logistic regression to more advanced methods, such as convolutional neural networks. You’ll see how you can use pretrained word embeddings, and you’ll squeeze more performance out of your model through hyperparameter optimization.
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