  A community needs assessment is a tool used by non-profits and government
agencies to quantify the strengths and issues of a community, allowing them to
allocate their resources better. Such approaches are transitioning towards
leveraging social media conversations to analyze the needs of communities and
the assets already present within them. However, manual analysis of
exponentially increasing social media conversations is challenging. There is a
gap in the present literature in computationally analyzing how community
members discuss the strengths and needs of the community. To address this gap,
we introduce the task of identifying, extracting, and categorizing community
needs and assets from conversational data using sophisticated natural language
processing methods. To facilitate this task, we introduce the first dataset
about community needs and assets consisting of 3,511 conversations from Reddit,
annotated using crowdsourced workers. Using this dataset, we evaluate an
utterance-level classification model compared to sentiment classification and a
popular large language model (in a zero-shot setting), where we find that our
model outperforms both baselines at an F1 score of 94% compared to 49% and 61%
respectively. Furthermore, we observe through our study that conversations
about needs have negative sentiments and emotions, while conversations about
assets focus on location and entities. The dataset is available at
https://github.com/towhidabsar/CommunityNeeds.
