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MarketPsych NLP Engine

An NLP-as-a-service platform for financial text analytics

MarketPsych NLP Engine

An NLP-as-a-service platform for financial text analytics

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NLP Engine, also known as SENT (Scoring Engine for Natural Text) is MarketPsych's internal natural language processing (NLP) engine, powering the MarketPsych Analytics products.

NLP Engine is now available via API and on-premises, with client-specific customizations. For further details and a demonstration please contact us.

At A Glance

NLP-as-a-service

MarketPsych’s Scoring Engine for Natural Text (SENT), is an NLP-as-a-service platform for financial analytics. SENT was developed to rapidly process large volumes of unstructured textual data from financial sources such as news, social media, fillings, transcripts, and brokerage research. Clients use SENT output (analytics) to generate standardized insights from their unstructured text. SENT identifies and tags millions of entities, tens of thousands of topics and events, and categorizes financial, ESG, and commodities sentiment. SENT can be deployed on premises or in the cloud. SENT is available on a per-client customization basis. For further details and demonstration please contact us.

Aggregated product sentiment using SENT output

Key Features

Entity Recognition

SENT's AI-driven Named Entity Recognition (NER) system tags millions of entities of over 20 types of entities, such as companies, products, currencies, bonds, ETFs, commodities, and places. It additionally links over 130,000 of those entities to LSEG’s PermID for seamless data integration.

Apple Org (PermID: 4295905573) announces new

iPhone Product in California Region

Topic and Event Insights

SENT covers an extensive profile of 1,000+ topics and 4,000+ events in categories such as finance, environmental issues, politics, law, and technology. Topics are organized in a hierarchical taxonomy for easy navigation.

BYD’s new electric model Topic: EV will be unveiled

Event: EV-Launch in August

Sentiment and Emotion Analysis

Leveraging three fine-tuned roBERTa classifiers, SENT quantifies the sentiment and emotional undertones in text, offering insights into market sentiment, ESG considerations, and commodity market dynamics, along with a spectrum of 10 emotional tones.

Sony’s earnings beat analyst expectations.

Financial sentiment: +98%

Analysts are dissatisfied with the company’s progress toward climate targets.

Disappointment: +26%, ESG sentiment: -98%

Popular client use cases

Real-Time Nowcasting

Feed real-time textual data into our system and receive instantaneous insights, enabling you to make informed decisions quickly.

Research Capabilities

Efficiently navigate through extensive libraries of documents to identify key relationships between entities, market trends, and thematic developments.

Regulatory Monitoring

Analyze regulatory changes with automated monitoring of regulatory texts.

ESG Insights

With coverage of over 200 ESG topics and a sentiment analysis model fine-tuned for ESG discourse, gain nuanced insights from ESG reports

Risk Identification

Automate detecting and monitoring risks within textual data. Perform KYC and monitor negative news with over 300 risk-related topics and events flagged for millions of entities.

Fraud Detection and Prevention

Enhance your fraud detection capabilities by analyzing transaction narratives, communication patterns, and reports.

Customer Insight and Engagement

Understand client preferences and concerns regarding your financial products and services.

Market Sentiment Analysis

Input real-time news, analyst reports, and social media feeds to gauge market sentiment.

Output And Accessibility

SENT outputs its analytics in JSON format, ensuring compatibility and ease of integration with existing systems. The output provides detailed, sentence-by-sentence insights.

Figure: Depiction of SENT's JSON output

Figure: Installation Workflow

SENT is available for deployment in multiple environments: on-premises, cloud-based, and via API. MarketPsych's tech team provides hosting advice, retrieval and visualization guidance for the detailed analytics.