Pideya Learning Academy

AI for Media Monitoring and Sentiment Analysis

Upcoming Schedules

  • Live Online Training
  • Classroom Training

Date Venue Duration Fee (USD)
24 Feb - 28 Feb 2025 Live Online 5 Day 3250
17 Mar - 21 Mar 2025 Live Online 5 Day 3250
07 Apr - 11 Apr 2025 Live Online 5 Day 3250
09 Jun - 13 Jun 2025 Live Online 5 Day 3250
07 Jul - 11 Jul 2025 Live Online 5 Day 3250
08 Sep - 12 Sep 2025 Live Online 5 Day 3250
20 Oct - 24 Oct 2025 Live Online 5 Day 3250
24 Nov - 28 Nov 2025 Live Online 5 Day 3250

Course Overview

In an age where public perception can pivot with a single viral post, and digital narratives unfold across millions of screens every second, the ability to monitor media and decode sentiment has become essential for any organization striving to remain relevant, trusted, and proactive. The rise of Artificial Intelligence (AI) has significantly transformed this landscape, offering powerful tools that help decipher vast, dynamic, and emotionally complex content from multiple platforms. Pideya Learning Academy introduces the comprehensive training program AI for Media Monitoring and Sentiment Analysis to equip professionals with the skills to harness the potential of AI for intelligent media tracking, nuanced sentiment interpretation, and strategic communication planning.
From news websites and forums to real-time social media updates, digital conversations form the backbone of public dialogue. Traditional media monitoring methods often fail to keep pace with this data volume and velocity. AI bridges this gap through Natural Language Processing (NLP), deep learning, and pattern recognition technologies that can interpret not just text, but the underlying emotion, sarcasm, urgency, and polarity of public discourse. This capability enables professionals to make data-informed decisions, preempt reputational threats, and shape narratives that align with stakeholder sentiment.
According to a recent MarketsandMarkets report, the global media monitoring tools market is expected to grow from USD 3.9 billion in 2023 to USD 6.9 billion by 2028, reflecting a CAGR of 11.8%. This surge is driven by increased demand for AI-powered sentiment analysis, reputation management, and real-time brand engagement. Furthermore, McKinsey & Company notes that businesses integrating AI into their communications strategies report up to 30% improved brand sentiment metrics and respond to public crises 40% faster than competitors.
The AI for Media Monitoring and Sentiment Analysis training from Pideya Learning Academy is carefully structured to provide deep technical insight while remaining accessible to professionals from both technical and non-technical backgrounds. Participants will explore how AI algorithms scan and analyze online content, how multilingual sentiment is detected across media types, and how to interpret these insights using AI-driven dashboards and visualizations. Within this structured journey, participants will gain direct exposure to the following essential capabilities:
Understanding the AI ecosystem for media and communication analytics, including NLP and machine learning models used in media tracking.
Applying sentiment analysis models to multi-lingual and multimodal content, enabling accurate interpretation across platforms and cultures.
Tracking brand perception using AI dashboards and visualization tools, enhancing reputation insights and stakeholder mapping.
Detecting early-warning signs for potential PR crises using predictive analytics, enabling faster response and mitigation strategies.
Leveraging AI to interpret media tone and public emotion from large datasets, improving campaign effectiveness and audience resonance.
Exploring ethics and bias challenges in AI-based sentiment evaluation, ensuring responsible, transparent, and fair analytical outcomes.
A key highlight of this program is its attention to responsible AI use in communication analytics, including how to mitigate bias in machine-generated interpretations and ensure transparency in decision-making processes. Participants will also examine real-world applications and case studies that demonstrate how AI has redefined media monitoring practices across sectors, from politics and retail to public policy and crisis communication.
By the end of the course, participants will understand how to align their media strategies with data-driven sentiment intelligence and integrate AI-based monitoring into their broader communication, marketing, and risk management frameworks. With a curriculum designed by industry experts and delivered through Pideya Learning Academy’s immersive and structured approach, this training empowers individuals and organizations to gain strategic clarity in an increasingly complex media environment.
Whether your focus is managing a brand’s global reputation, understanding consumer sentiment, or informing policy and crisis decisions, this training provides the insight and tools needed to stay ahead of the digital conversation—confidently, ethically, and intelligently.

Course Objectives

After completing this Pideya Learning Academy training, the participants will learn to:
Decode how AI technologies transform traditional media monitoring frameworks
Build a foundational understanding of sentiment analysis and its linguistic models
Interpret sentiment outputs from machine learning and NLP engines
Design strategic media dashboards incorporating sentiment and trend analysis
Implement multi-source media tracking using AI-based tools
Identify false positives, data bias, and ethical implications in media intelligence
Evaluate case studies showcasing AI-led sentiment insights in global communications
Structure organization-wide monitoring processes around AI analytics

Personal Benefits

Gain future-ready skills in AI-powered communications analytics
Improve interpretation of audience emotions, tone, and media context
Learn to design dashboards and sentiment maps using modern AI platforms
Expand analytical thinking and cross-functional reporting capabilities
Build competitive knowledge in emerging communication technologies

Organisational Benefits

Strengthen media relations strategy with real-time AI-powered intelligence
Reduce reputational risk through early sentiment detection
Enhance public engagement strategies using AI-verified audience feedback
Align corporate communication with evolving public discourse and media sentiment
Support marketing, PR, and compliance teams with intelligent media tracking capabilities

Who Should Attend

Communication Directors and PR Managers
Media Analysts and Brand Managers
Government Communication Officers
Data Scientists and AI Specialists in Media
Risk and Crisis Management Teams
Corporate Affairs and Strategy Professionals
Academics and Researchers in Media Studies
Training

Course Outline

Module 1: Introduction to AI in Media Analytics
Evolution of media monitoring technologies Core components of AI in media analysis Overview of sentiment analysis and its role in media tracking Key terminologies: NLP, NLU, ML, classifiers Public vs. proprietary AI tools Industry adoption trends and benchmarks
Module 2: Foundations of Sentiment Analysis
Rule-based vs. ML-based sentiment models Tokenization, stemming, and lemmatization Emotion detection and polarity classification Multi-lingual sentiment detection Use of sentiment lexicons and scoring algorithms Contextual vs. literal sentiment
Module 3: Data Collection and Media Input Streams
Structuring input from news portals, blogs, and social media Real-time media scraping methodologies API integration with monitoring platforms Handling structured and unstructured content Filtering noise and spam in datasets Metadata tagging for content classification
Module 4: NLP Models for Sentiment Interpretation
Overview of BERT, GPT, and other transformer models Named entity recognition in sentiment modeling Sentiment shifts and change detection Emotion classifiers: joy, anger, fear, sadness Sarcasm and irony in text analysis Pre-training vs. fine-tuning models
Module 5: AI Dashboards and Visualization Techniques
Designing custom sentiment heat maps Real-time data streaming to dashboards Interactive sentiment timelines and bubble charts Filterable insights by source, tone, region, or issue Role of visual storytelling in media intelligence Exportable insights for executive reporting
Module 6: Predictive Media Monitoring
Time-series analysis of sentiment trends Crisis prediction and event correlation Sentiment anomalies and automated alerts Early detection of misinformation patterns AI-triggered workflows for reputation escalation Feedback loop design for continuous learning
Module 7: Ethical, Legal, and Bias Considerations
AI bias in sentiment interpretation Ethical boundaries in media surveillance Transparency in algorithmic predictions Jurisdictional differences in media monitoring laws Managing public backlash from AI errors Mitigation strategies for false sentiment tagging
Module 8: Use Cases and Sectoral Applications
Corporate reputation management Political campaign sentiment tracking Customer satisfaction analytics in media Stakeholder sentiment in ESG reporting Regulatory sentiment in legal media Academic and journalistic research applications
Module 9: Designing Your AI-Driven Monitoring Strategy
Frameworks for AI integration in existing systems KPI mapping for sentiment-based insights AI vendor selection and customization pathways Setting up cross-functional media intelligence teams Measuring impact and performance of sentiment analysis Roadmap for AI maturity in media functions

Have Any Question?

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