1/ Financial sentiment is evolving from simple keyword counts to models that can reason across text, tone, and visuals. Our latest research explores this shift. Introducing: Adaptive Financial Sentiment Intelligence👇

2/ At the core of the framework are 3 innovations: 🧠 AIAP – Annotator Instruction Assisted Prompting 🔎 RAG – Retrieval Augmented Generation 🎧📊 Multimodal Sentiment Modeling Let’s break them down ⬇️

3/ AIAP embeds analyst-style reasoning directly into the model. Instead of guessing tone or intent, the model follows real annotation logic improving consistency and reducing interpretation errors. Accuracy boost: +9–10%. That’s massive for finance.

4/ RAG unlocks real-time market awareness. The model doesn’t rely on outdated training data it retrieves relevant filings, news, and developments on the fly. No more stale sentiment. No more blind spots.

5/ Multimodal Modeling fuses text, tone, and visuals: 📝 text sentiment 🎧 vocal tone from earnings calls 📊 charts/tables signals 🖼 contextual visuals The system reads the market like a human analyst but faster, and with more evidence.

6/ When combined, these layers produce sentiment outputs that are: ✅ evidence-backed ✅ explainable ✅ up-to-date ✅ high-confidence A true evolution from static models to adaptive reasoning systems.

7/ What does this mean for market intelligence? • Better hedging signals • More reliable sentiment divergence alerts • Improved risk assessment • Stronger alignment between human and AI interpretation This is AI that thinks, not just predicts.