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Exploring Modern Sentiment Analysis Approaches in Python

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Manage episode 456625110 series 2637014
内容由Real Python提供。所有播客内容(包括剧集、图形和播客描述)均由 Real Python 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/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

243集单集

Artwork
icon分享
 
Manage episode 456625110 series 2637014
内容由Real Python提供。所有播客内容(包括剧集、图形和播客描述)均由 Real Python 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/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

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