AI Enrichment
Understand how Lucidya's AI enriches your data : From sentiment analysis and spam detection to Arabic dialect understanding.
8 articles
- How Lucidya AI worksAn overview of the AI models and engines powering Lucidya's enrichment capabilities — how data is processed, what intelligence is applied, and how AI-driven insights are generated across the platform.
- How Lucidya AI works (FAQs)Common questions about Lucidya's AI infrastructure, model behavior, accuracy, and how AI enrichment applies across different products and data sources.
- Sentiment analysis : How to interpret results (Arabic & English)A guide to understanding Lucidya's sentiment analysis output — how positive, neutral, and negative classifications are determined, what influences results, and how to interpret scores in Arabic and English content.
- Sentiment analysis : How to interpret results (Arabic & English) - FAQsFrequently asked questions about sentiment scoring, edge cases, language-specific behavior, and how to act on sentiment data in your reports and monitors.
- Arabic dialect understanding: What’s supported and how to use itAn overview of Lucidya's Arabic dialect recognition capability — which dialects are supported, how the model identifies them, and how dialect understanding improves analysis accuracy across MENA regions.
- Arabic dialect understanding: What’s supported and how to use it (FAQs)Common questions about dialect coverage, model accuracy across regions, and how dialect understanding interacts with sentiment analysis and other AI enrichment features.
- Spam detection : What gets filtered and what doesn'tA reference explaining how Lucidya's spam detection model works — what types of content are flagged and filtered, what passes through, and how filtering affects your monitor data.
- Spam detection: What gets filtered and what isn’t (FAQs)Frequently asked questions about spam detection behavior, false positives, filter thresholds, and how to manage content that was incorrectly filtered or missed.