Search a title or topic

Over 20 million podcasts, powered by 

Player FM logo
Artwork

Content provided by Real Python. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Real Python or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
Player FM - Podcast App
Go offline with the Player FM app!

Exploring Modern Sentiment Analysis Approaches in Python

1:13:09
 
Share
 

Manage episode 456625110 series 2637014
Content provided by Real Python. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Real Python or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

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.

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

Show Links:

Level up your Python skills with our expert-led courses:

Support the podcast & join our community of Pythonistas

  continue reading

253 episodes

Artwork
iconShare
 
Manage episode 456625110 series 2637014
Content provided by Real Python. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Real Python or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

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.

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

Show Links:

Level up your Python skills with our expert-led courses:

Support the podcast & join our community of Pythonistas

  continue reading

253 episodes

All episodes

×
 
Loading …

Welcome to Player FM!

Player FM is scanning the web for high-quality podcasts for you to enjoy right now. It's the best podcast app and works on Android, iPhone, and the web. Signup to sync subscriptions across devices.

 

Copyright 2025 | Privacy Policy | Terms of Service | | Copyright
Listen to this show while you explore
Play