Scholarlink

The burgeoning startup ecosystem necessitates robust and efficient methods for evaluating the credibility and historical performance of founders and their ventures. Traditional due diligence processes are often manual, time-consuming, and susceptible to information overload and inherent biases. This paper presents “Founder Verifier,” an Artificial Intelligence (AI) driven web application designed to automate and enhance the initial stages of founder reputation analysis. The system integrates web scraping capabilities with Natural Language Processing (NLP) techniques to provide a multifaceted assessment. Key functionalities include sentiment analysis of web-derived text using the NLTK VADER lexicon, Named Entity Recognition (NER) for geographical context extraction via spaCy, keyword-based potential controversy detection, and assimilation of failure/industry insights. These analyses culminate in a consolidated web report featuring a heuristically derived reputation score, categorized sentiment snippets, links to relevant external profiles, and downloadable PDF summaries. An operational dashboard further provides analytics on system usage and observed trends. This paper details the system's architecture, the methodologies employed for data acquisition and multi-modal analysis, the formulation of the reputation score, and discusses the structure and utility of its outputs. The Founder Verifier aims to provide a rapid, data-driven preliminary assessment tool for stakeholders in the startup ecosystem.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Founder Verifier: An AI-Driven System for Automated Startup Founder Reputation Analysis and Verification

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Radhika V. Kulkarni,
  • Gaurav Sulsule,
  • Sanika Kolawale,
  • Urmila Kakarwal,
  • Prathmesh Deshmukh,
  • Prakash Sharma

摘要

The burgeoning startup ecosystem necessitates robust and efficient methods for evaluating the credibility and historical performance of founders and their ventures. Traditional due diligence processes are often manual, time-consuming, and susceptible to information overload and inherent biases. This paper presents “Founder Verifier,” an Artificial Intelligence (AI) driven web application designed to automate and enhance the initial stages of founder reputation analysis. The system integrates web scraping capabilities with Natural Language Processing (NLP) techniques to provide a multifaceted assessment. Key functionalities include sentiment analysis of web-derived text using the NLTK VADER lexicon, Named Entity Recognition (NER) for geographical context extraction via spaCy, keyword-based potential controversy detection, and assimilation of failure/industry insights. These analyses culminate in a consolidated web report featuring a heuristically derived reputation score, categorized sentiment snippets, links to relevant external profiles, and downloadable PDF summaries. An operational dashboard further provides analytics on system usage and observed trends. This paper details the system's architecture, the methodologies employed for data acquisition and multi-modal analysis, the formulation of the reputation score, and discusses the structure and utility of its outputs. The Founder Verifier aims to provide a rapid, data-driven preliminary assessment tool for stakeholders in the startup ecosystem.