In the era of data-driven decision making, cities and communities are increasingly seeking ways to effectively gather insights from public feedback and comments to shape their research and development initiatives. Town hall community meetings serve as a valuable platform for citizens to express their opinions, concerns, and ideas about various aspects of city life. In this study, we aim to explore the effectiveness of different keyword extraction tools and similarity matching algorithms in matching town hall community comments with city strategic plans and current research opportunities. We employ KPMiner, TopicRank, MultipartiteRank, and KeyBERT for keyword extraction, and evaluate the performance of cosine similarity, word embedding similarity, and BERT-based similarity for matching the extracted keywords. By combining these techniques, we aim to bridge the gap between community feedback and research initiatives, enabling data-driven decision-making in urban development. Our findings will provide valuable insights for more inclusive and informed strategies, ensuring that citizen opinions and concerns are effectively incorporated into city planning and development efforts.