Скачать или смотреть Sentiment Analysis & Text Classification: Teaching AI to Understand Emotion & Categories!

Welcome back to our NLP module! In Lecture 3: Sentiment Analysis & Text Classification, we explore two of the most common and practical tasks in Natural Language Processing. Once text is represented numerically, AI can begin to extract powerful insights from it.

You'll learn how AI can determine the emotional tone of text (positive, negative, neutral) through Sentiment Analysis, crucial for understanding customer feedback. We'll then broaden our scope to Text Classification, where AI assigns predefined categories to text, from filtering spam to organizing news articles.

In this video, you will learn:

What Sentiment Analysis is and its widespread applications (customer feedback, social media monitoring).

Challenges in sentiment analysis, including sarcasm, irony, and nuanced language.

The broader concept of Text Classification: assigning predefined categories or labels to text.

Practical examples of text classification: Spam Detection, Topic Categorization (e.g., news articles), and Urgency Detection in customer support.

The typical workflow for text classification: Text Preprocessing → Feature Extraction → Model Training → Prediction.

Common Machine Learning and Deep Learning models used for these tasks.

Master these powerful tools to extract valuable insights from vast amounts of unstructured text data, automate processes, and enhance decision-making!

👍 If you're ready to make AI understand the tone and topic of text, hit the like button and subscribe for more practical NLP skills!

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