A methodology for analyzing financial needs hierarchy from social discussions using LLM

2026-02-06

AI Summary

This study addresses the limitations of traditional static surveys by dynamically identifying and structuring individuals’ financial needs hierarchies from social media discussions. To this end, we propose a novel approach that integrates large language models (LLMs) with natural language processing techniques to automatically extract implicit financial needs from user-generated text and organize them into a hierarchical framework ranging from short-term basic requirements to long-term financial goals. Our method not only confirms the pronounced hierarchical nature of financial needs expressed in online discourse but also uncovers distinctive content characteristics and evolutionary patterns of related topics. This work introduces a scalable, data-driven paradigm for analyzing financial behavior, offering new avenues for understanding consumer financial decision-making in digital environments.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Web Mining and Content Analysis: Bridging structured and unstructured data

Abstract

This study examines the hierarchical structure of financial needs as articulated in social media discourse, employing generative AI techniques to analyze large-scale textual data. While human needs encompass a broad spectrum from fundamental survival to psychological fulfillment financial needs are particularly critical, influencing both individual well-being and day-to-day decision-making. Our research advances the understanding of financial behavior by utilizing large language models (LLMs) to extract and analyze expressions of financial needs from social media posts. We hypothesize that financial needs are organized hierarchically, progressing from short-term essentials to long-term aspirations, consistent with theoretical frameworks established in the behavioral sciences. Through computational analysis, we demonstrate the feasibility of identifying these needs and validate the presence of a hierarchical structure within them. In addition to confirming this structure, our findings provide novel insights into the content and themes of financial discussions online. By inferring underlying needs from naturally occurring language, this approach offers a scalable and data-driven alternative to conventional survey methodologies, enabling a more dynamic and nuanced understanding of financial behavior in real-world contexts.

Problem

• financial needs
• hierarchy
• social media
• behavioral analysis
• large language models

Innovation

• large language models
• financial needs hierarchy
• social media analysis
• generative AI
• computational behavioral science