KC Tseng
An evidence-based, customer-centric, and deliberative strategist with lean agility and pragmatism. Driving data insights, agile innovation, and resilient communities in a VUCA world.
Session
In today's digital era, policy discussions within communities are becoming increasingly frequent, raising the question of how to effectively analyze and apply these discussions to support decision-making. This sharing proposes a simple and sustainable text analysis framework based on Python, designed to help community members and policymakers extract valuable insights from discussion data. The proposed framework integrates text mining techniques and sentiment analysis, and dynamic dashboards, utilizing methods such as topic modeling and text similarity calculations to automatically identify key topics, sentiment trends, and similar suggestions within discussions.
While still in development, this project emphasizes the sustainability of the approach. Leveraging Python's open-source tools and libraries, I hope to provide a modular analysis process that allows users of varying technical backgrounds to quickly adopt and apply it flexibly to various community policy discussion contexts, and help enable community members to engage more effectively and influence policy decisions.
As this project is in its early stages, ongoing efforts focus on refining the methodology and validating its applicability in real-world scenarios. The potential applications combine policy analysis with data science methodologies.