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.

借助先进的自然语言处理方法，通过社交媒体对话的认知数据，识别、提取和分类社区需求和资产的任务呈现了挑战，进而填补了目前文献中关于社区成员如何讨论社区需求和资产的计算分析研究空白。通过对来自Reddit的3,511个对话进行众包注释，我们评估了一个以话语级别分类的模型，与情感分类和流行的大型语言模型（在零样本设置中）相比，我们发现我们的模型在F1分数上分别达到了94%、49%和61%，此外，通过研究我们还观察到有关需求的对话具有负面情绪和情感，而有关资产的对话则侧重于位置和实体。该数据集可在此 https URL 进行访问。

社区需求与资源：社区对话的计算分析