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Course Outline

Introduction to Sentiment Analysis

  • Fundamental concepts of sentiment analysis.
  • Challenges and opportunities inherent in sentiment analysis.
  • Overview of LLMs and their functional capabilities.

LLMs and Natural Language Understanding

  • In-depth exploration of LLM architecture.
  • Leveraging LLMs for context and sentiment comprehension.
  • Data preprocessing techniques for sentiment analysis.

Developing Sentiment Analysis Models with LLMs

  • Training LLMs specifically for sentiment analysis tasks.
  • Fine-tuning models for specialized domains.
  • Practical exercises focused on model training.

Social Media Analysis Using LLMs

  • Data collection strategies for social media analysis.
  • Real-time sentiment tracking across social platforms.
  • Case studies demonstrating social sentiment analysis.

Sentiment Analysis in Customer Feedback

  • Deriving insights from customer reviews and survey data.
  • Enhancing customer service through sentiment analysis.
  • Workshop on analyzing feedback data.

Advanced Topics in Sentiment Analysis

  • Addressing sarcasm, irony, and complex emotional expressions.
  • Performing cross-language sentiment analysis.
  • Emerging trends in sentiment analysis utilizing LLMs.

Ethical Considerations and Bias Mitigation

  • Exploring ethical implications of sentiment analysis.
  • Identifying and reducing bias within models.
  • Ensuring responsible application of sentiment analysis.

Project and Assessment

  • Analyzing sentiment from a selected dataset.
  • Peer reviews and collaborative group discussions.
  • Final assessment and constructive feedback.

Summary and Next Steps

Requirements

  • Familiarity with fundamental machine learning concepts.
  • Practical experience in preprocessing and analyzing text data.
  • Proficiency in Python programming.

Target Audience

  • Data scientists and analysts.
  • Marketing professionals.
  • Product managers.
 21 Hours

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Price per participant

Provisional Upcoming Courses (Require 5+ participants)

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