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Exploring Sentiment Analysis in Machine Learning
This chapter examines the intricacies of sentiment analysis, focusing on its applications in machine learning and the evolution from simple methods to modern language models. It highlights key concepts such as dimensional emotion classification, the impact of context on analysis outcomes, and the importance of linguistic nuances. Additionally, the chapter discusses tools used for sentiment analysis, emphasizing the distinction between objective and subjective feedback in various text forms.
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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