ProntoNLP Expert Network Analytics

The ProntoNLP Expert Network Analytics dataset leverages a proprietary, fine-tuned Large Language Model (LLM) developed in partnership with Third Bridge to extract structured insights from expert network call transcripts. By employing advanced Natural Language Processing, the model identifies discussed companies, assesses the relevance and importance of associated events, and separates valuable market signals from noise. This specialized solution overcomes the challenges of analyzing unstructured, question-and-answer conversation formats, converting complex dialogue into clean, actionable intelligence.

This dataset enables investment professionals to seamlessly compare sentiment across peer companies within the same sector when multiple entities are discussed in a single call. Users can easily track competitive dynamics by analyzing independent specialist commentary regarding a target company alongside its rivals, customers, and suppliers. Additionally, the data helps surface early market signals by highlighting shifts in expert sentiment around specific events or themes, while allowing analysts to map a company across the entire value chain by aggregating disparate mentions into a continuous, attributable sentiment view.

This dataset includes:

- Entity-Level Attribution: Proprietary technology that isolates individual companies discussed within a single answer, ensuring sentiment is accurately mapped to the correct entity
- Company-Specific Sentiment Signals: Clean, individual sentiment and importance scores for each referenced company, avoiding blended or diluted scores
- Structured Event Relevance: Classification and scoring of key events and themes mentioned during the expert conversations
- High-Quality Source Transcripts: Curated expert network call transcripts provided by Third Bridge's specialist team

原始信息汇总

数据集名称

ProntoNLP Expert Network Analytics

提供方

S&P Global Market Intelligence

概述

该数据集利用专有的、经过微调的大语言模型（LLM），与Third Bridge合作开发，用于从专家电话会议记录中提取结构化洞察。通过先进的自然语言处理技术，模型能够识别被讨论的公司、评估相关事件的相关性和重要性，并从噪声中分离出有价值的市场信号，将复杂的问答对话转化为清晰、可操作的情报。

包含的内容

• 实体级归因：专有技术，用于隔离单条回答中讨论的各个公司，确保情感分析准确映射到正确的实体。
• 公司特定情感信号：为每个被提及的公司提供清晰、独立的情感和重要性评分，避免分数混合或稀释。
• 结构化事件相关性：对专家对话中提及的关键事件和主题进行分类和评分。
• 高质量源文本：由Third Bridge专家团队提供的精选专家电话会议记录。

业务需求

• 市场分析：通过分析专家情绪及其围绕特定事件的转变，帮助投资专业人士追踪竞争动态，发现早期市场信号。
• 情感分析：使用微调后的LLM将非结构化的专家会议记录转化为清晰的实体级情感评分，实现对竞争对手和行业市场趋势的精确追踪。
• 基本面分析：获取结构化的专家评论和价值链映射，帮助基本面分析师评估单个公司、同行表现以及特定公司事件的影响。

更新频率

每日（Daily）

分类

• 金融（Financial）
• 基本面分析（Fundamental Analysis）
• 市场分析（Market Analysis）
• 情感分析（Sentiment Analysis）

联系方式

• 销售邮箱：[e-mail]
• 支持邮箱：[e-mail]