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Sentiment Analysis and Predictive Analytics

Summary

This chapter explores how AI-powered analytics tools enable IR teams to understand investor sentiment and predict market responses. You'll learn to use sentiment analysis tools, natural language processing (NLP), text mining methods, social media monitoring, news sentiment analysis, analyst report insights, feedback analysis, sentiment scoring models, and real-time sentiment data. The chapter also covers predictive analytics, forecasting investor behavior, predicting market response, and modeling investor behavior. These analytical capabilities enable data-driven IR strategies and proactive stakeholder engagement.

Concepts Covered

This chapter covers the following 20 concepts from the learning graph:

  1. Sentiment Analysis Tools
  2. Natural Language Processing
  3. Text Mining Methods
  4. Monitoring Social Media
  5. News Sentiment Analysis
  6. Analyst Report Insights
  7. Analyzing Feedback
  8. Sentiment Scoring Models
  9. Real-Time Sentiment Data
  10. Predictive Analytics
  11. Forecasting Investor Behavior
  12. Predicting Market Response
  13. Modeling Investor Behavior
  14. Trading Pattern Analysis

Prerequisites

This chapter builds on concepts from:

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