Summary
Measuring impact is easy when there are quantifiable data points available – financial metrics, survey measurements, or even binary flags. But how do you answer questions about process or impact when there is very little quantifiable data available? Join us for a fireside chat (webinar) as we dive into leveraging Natural Language Processing (NLP) models to analyze machine-readable transcripts for key insights related to company support of the LGBTQ+ community.
In this session, you will learn:
- How to craft a premise which can target the most meaningful insights
- Best practices for training the model to avoid excess noise
- Methods for analyzing the results and uncovering additional questions
- Opportunities to add firmographics or other reference data to tell a broader story
Authors of the paper, Assessing Companies' LGBTQ+ Engagement: The Balance Between Public Mention and Supportive Action, will be joined by ProntoNLP, a partner with a low-code NLP platform to give businesses greater access to AI tools.
Speakers
S&P Global Market Intelligence
Emily Jasper
S&P Global Market Intelligence
Director Data and Research
Emily is a product management professional at S&P Global Market Intelligence, specializing in solutions for Sales and Marketing, including CRM data. She oversees the enhancement of Salesforce offerings and collaborates on Xpressfeed™ and API products. With a decade of experience spanning manufacturing, advertising, and enterprise software, she's recognized as one of Virginia's Top 50 Women Leaders.
ProntoNLP
Ilan Attar
Data Science & Product Team Lead
Ilan Attar is the Data Scientist and Product Team Lead at ProntoNLP, specializing in natural language processing (NLP) within the financial sector. With an MBA in Big Data Analytics from The Hebrew University of Jerusalem, Ilan has a strong foundation in developing data-driven solutions from research to production. His experience spans across building NLP models, implementing machine learning strategies, and creating tools to analyze investment strategies. Ilan’s unique blend of technical and business expertise allows him to bridge the gap between data science and finance.