AI-driven Sentiment Scoring Systems

I see nobody doing this with AI voice agents. So I did. This is unlocking a whole new layer of intelligence on your AI voice calls. What is it? Sentiment Anlalysis. Why does this matter? Because most businesses are sitting on a goldmine of voice data... but they’re not extracting the emotional signals that drive real outcomes. Here’s where sentiment analysis actually adds value: ✅ Customer Experience Monitoring Spot unhappy customers early. Trigger an automatic follow-up if a call turns negative. ✅ Agent Performance Tracking See how sentiment shifts across reps, scripts, or time. Is your team actually creating positive experiences? ✅ Trend Recognition Negative sentiment = higher churn? Now you've got predictive insights. ✅ Training & QA Flag poor sentiment calls for review. Let AI highlight the moments that caused friction. But it's not always worth your time... Sentiment analysis is useless if: → You're not acting on the data. → Your calls are too short or robotic. → You don’t have enough volume to find patterns. → Your domain needs custom sentiment tuning (sarcasm, mixed languages, etc.). Want to make it actually useful? Here’s how: → Link sentiment to outcomes like conversions or renewals. → Create real-time alerts or dashboards for your team. → Fine-tune the model on your transcripts, not generic ones. → Combine it with other signals like talk-time, interruptions, and keywords. The emotional layer of your calls is where the real insight lives. Curious how this works in practice? I’m happy to show what I built today. Drop a “curious” below or shoot me a message.

AI for Finance Leaders: TL;DR Edition. AI for Smarter Forecasting: The Power of Sentiment Analysis We all wish we had more time to dive into the research on AI and think about how it will impact us, so today we’re doing just that.  Xiaowei Zhao, Yong Zhou, Xiujuan Xu, Yu Liu recently published a paper called “Extensible Multi-Granularity Fusion Network for Aspect-based Sentiment Analysis” or more simply “Super Smart Tech That Knows What People Really Think”. Let’s break it down to understand what the technology means and how I imagine it could impact the role of finance in the organization. What’s the paper about?   Imagine a super smart tool that reads through thousands of online reviews to find out exactly what people love or don't about a movie, down to the smallest details like the storyline or the graphics. This magic tool is powered by the latest tech called the EMGF (Extensible Multi-Granularity Fusion) network, which is like a detective that can understand feelings and opinions in writing. It's not just about knowing if people liked the movie, but understanding every single part they talked about. Why does it matter for finance? It could be easy to dismiss this technology in finance, but this gives us the ability to analyze written subjective content in mass to create more consistent and accurate forecasts. Here are a few ways that I could see finance tools using this tech to improve your workflow. Practical applications: CRM & Call recording Insights  Imagine using this technology to read through every recorded call with a prospect or customer. Often in sales forecasting, reps use their gut to determine whether they think a deal will move to the next stage. Once you have more than one rep, the consistency starts to plummet. Some are sandbagging, some are looking at the deal with rose-colored glasses, and the results can be all over the place. If AI were able to read through these conversations, it could consistently use sentiment analysis to apply scoring to sales conversations, potentially leading to more accurate and consistent forecasting. Practical applications: Decoding Market Trends We’re also often trying to pull market signals into our forecasts. The problem is there’s so much external data that’s difficult to distill into any kind of useful signal. With the power of EMGF network analysis, we may start to see AI dive into a sea of data from social media, news, etc. to spot the small and large trends that are relevant to our specific markets. It would be like having a map that shows where consumer interests are heading, allowing you to navigate your business strategy with precision and foresight. This insight could further increase our forecast accuracy. How do you see AI-driven sentiment analysis changing your approach to financial analysis and forecasting? #finance #ai #cfo

Reading interesting paper from Nature.Com that recommends advanced artificial intelligence system to analyze how patients feel about their medications based on their online reviews. The researchers developed and compared multiple machine learning (ML), deep learning (DL), and ensemble models to predict patient sentiments from medication reviews Main Results The researchers created a model called DL_ENS that achieved remarkable accuracy: ·  92.26% accuracy for classifying reviews as positive or negative ·  92.18% accuracy for classifying reviews as positive, neutral, or negative ·  90.31% accuracy for predicting detailed 1-10 ratings How It Works The system analyzes patient reviews from drugs.com by: ·  Processing text from over 213,000 medication reviews ·  Understanding the context and meaning of medical terms ·  Identifying key words that indicate patient satisfaction or dissatisfaction ·  Providing clear explanations for its predictions Practical Benefits For Patients: ·  Better understanding of other patients' experiences ·  More informed decisions about medications ·  Access to organized feedback from real users For Doctors: ·  Quick insights into patient experiences ·  Better understanding of medication side effects ·  Data-driven support for prescribing decisions Most Reviewed Conditions The top five most discussed medical conditions in the reviews were: ·  Birth control ·  Depression ·  Pain ·  Anxiety ·  Acne Advantages Over Previous Systems The new system outperforms earlier methods by: ·  Being more accurate in predicting patient sentiments ·  Understanding medical terminology better ·  Providing clear explanations for its decisions ·  Working with both detailed ratings and simple positive/negative feedback This research represents unique approach in medication sentiment analysis, offering a practical tool for healthcare professionals while maintaining transparency and interpretability in its decision-making process #Medicationreviews #Patientsentimentanalysis #Machinelearning #Deeplearning #Ensemblelearning #Clinicaldecisionsupport #Patientfeedback #Drugreviews #Treatmenteffectiveness #Medicallexicon #Healthcareinformatics #Textpreprocessing #Featureextraction #Crossvalidation #Hyperparameteroptimization #Modelinterpretability #Performancemetrics Source: https://lnkd.in/eqBXkRva Disclaimer: The opinions are mine and not of employer's

💭 AI is transforming finance—but is it truly reshaping the core of Quant Finance beyond just trading? While algorithmic trading gets most of the attention, AI is making a deeper impact in risk modeling, derivatives pricing, and portfolio optimization. 1️⃣ Sentiment Analysis for Market Forecasting (LLMs & NLP Models) 👉 Why it matters: Markets don’t move on fundamentals alone—investor sentiment drives volatility. AI-powered NLP can process news, earnings calls, analyst reports, and social media to detect sentiment shifts in real time, providing traders with early signals before price movements occur. 🛠 Real Models in Action: ✔ FinBERT (Hugging Face) – A finance-focused NLP model trained on earnings reports and financial news to extract sentiment insights. ✔ GPT-4 fine-tuned for finance – Used in hedge funds to generate sentiment-based trading signals and volatility forecasts. ✔ BloombergGPT – Specialised for market-related NLP tasks, enhancing automated financial analysis. 2️⃣ AI for Derivatives Pricing & Risk Management (Deep Learning & Stochastic Models) 👉 Why it matters: Traditional pricing methods rely on Monte Carlo simulations and PDE-based models, which can be computationally expensive and slow. AI accelerates pricing and hedging strategies by learning risk-neutral representations and improving predictive accuracy for exotic derivatives. 🛠 Real Models in Action: ✔ Neural SDEs (Stochastic Differential Equations) – AI-driven models that learn underlying stochastic processes for better risk-neutral pricing. ✔ Physics-Informed Neural Networks (PINNs) – AI-enhanced solvers that significantly speed up complex derivatives pricing calculations. ✔ Deep Hedging Models – AI-powered dynamic hedging strategies that adjust in real time, outperforming traditional Black-Scholes delta hedging in volatile markets. 3️⃣ AI for Dynamic Portfolio Optimization (Reinforcement Learning & Bayesian ML) 👉 Why it matters: Traditional Mean-Variance Optimization (MVO) assumes fixed return distributions and correlations, which often break down during market shifts. AI allows adaptive asset allocation, helping investors manage risk dynamically and rebalance portfolios in response to changing market regimes. 🛠 Real Models in Action: ✔ Reinforcement Learning Portfolio Management (RLPM) – Uses deep Q-learning and policy gradient methods to find optimal asset allocation strategies under different market conditions. ✔ Bayesian Neural Networks (BNNs) – Introduces uncertainty estimation in return predictions, improving risk-aware decision-making. ✔ Hierarchical Risk Parity (HRP) – AI-powered clustering of assets for better diversification and tail-risk mitigation, outperforming classical Markowitz models. #AI #QuantFinance #MachineLearning #RiskManagement #DerivativesPricing #PortfolioOptimization #SentimentAnalysis #FinancialModeling #FinTech #HedgeFunds #MarketRisk #FinanceJobs