通过主题分类的半自动提取识别NSCLC患者的社会对话结构
Identifying Structures in Social Conversations in NSCLC Patients through the Semi-Automatic extraction of Topical Taxonomies
摘要：
探讨社会对话，以解决病人的需求是一个重要的分析任务，许多学术出版物都有助于填补这一领域的知识空白。主要的困难仍然是无法将这些贡献转化为制药行业可以利用的实用过程，以便从社交媒体数据中产生洞察力，由于其庞大的数量和噪音，社交媒体数据可以被认为是当今最具挑战性的信息来源之一。这项研究是基于Scott Spangler和Jeffrey Kreulen的工作，并将其应用于通过提取能够捕获健康相关网站社交对话中的潜在知识的主题分类来识别社交媒体中的结构。自动识别和生成一个分类从社会对话的机制开发和压力测试使用的公共数据从媒体网站集中在癌症患者及其家属的需求。此外，一种新的方法，用于生成类别的标签和确定的最佳数量的类别，扩展了斯科特和杰弗里的研究在一个有意义的方式。我们假设读者熟悉分类法，它们是什么以及如何使用它们。
英文摘要：
The exploration of social conversations for addressing patient's needs is an important analytical task in which many scholarly publications are contributing to fill the knowledge gap in this area. The main difficulty remains the inability to turn such contributions into pragmatic processes the pharmaceutical industry can leverage in order to generate insight from social media data, which can be considered as one of the most challenging source of information available today due to its sheer volume and noise. This study is based on the work by Scott Spangler and Jeffrey Kreulen and applies it to identify structure in social media through the extraction of a topical taxonomy able to capture the latent knowledge in social conversations in health-related sites. The mechanism for automatically identifying and generating a taxonomy from social conversations is developed and pressured tested using public data from media sites focused on the needs of cancer patients and their families. Moreover, a novel method for generating the category's label and the determination of an optimal number of categories is presented which extends Scott and Jeffrey's research in a meaningful way. We assume the reader is familiar with taxonomies, what they are and how they are used.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
