Dec 20, 2024 1h 13m
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.
Episode Sponsor:
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.
Topics:
- 00:00:00 – Introduction
- 00:02:31 – Conference talks in 2024
- 00:04:23 – Background on sentiment analysis and studying feelings
- 00:07:09 – What led you to study emotions?
- 00:08:57 – Dimensional emotion classification
- 00:10:42 – Different types of sentiment analysis
- 00:14:28 – Lexicon-based approaches
- 00:17:50 – VADER – Valence Aware Dictionary and sEntiment Reasoner
- 00:19:41 – TextBlob and subjectivity scoring
- 00:21:48 – Sponsor: Sentry
- 00:22:52 – Measuring sentiment of New Year’s resolutions
- 00:27:28 – Lexicon-based approaches links for experimenting
- 00:28:35 – Multiple language support in lexicon-based packages
- 00:35:23 – Machine learning techniques
- 00:39:20 – Tools for this approach
- 00:42:54 – Video Course Spotlight
- 00:44:15 – Advantages to the machine learning models approach
- 00:45:55 – Large language model approach
- 00:48:44 – Encoder vs decoder models
- 00:52:09 – Comparing the concept of fine-tuning
- 00:56:49 – Is this a recent development?
- 00:58:08 – Ways to practice with these techniques
- 01:00:10 – Do you find this to be a promising approach?
- 01:07:45 – Resources to practice with all the techniques
- 01:11:06 – Upcoming conference talks
- 01:11:56 – Thanks and goodbye
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