USE OF NATURAL LANGUAGE PROCESSING TO IDENTIFY LINGUISTIC MARKERS OF COPING

描述（由申请人提供）：了解作用机制是改善癌症和其他慢性疾病的心理社会干预的关键。在癌症中，情绪表达已被确定为心理社会干预对患者报告结果的影响的一个可能的中介。然而，对癌症和其他慢性疾病的心理调整机制的科学评估受到自我报告措施的限制。由于自我护理资源，点对点网络和最近形式的心理社会干预越来越多地在线提供，语言和行为数据可以用来描述内部应对过程，社会互动和其他明显的行为。目前很少有工具可用于利用文本作为潜在的数据源，现有工具的信号检测指数为这些方法留下了相当大的改进空间（Bantum和Owen，2009年）。在本研究中，自然语言处理和计算语言学的其他工具将用于开发机器学习分类器来识别电子文本数据中的情感表达。这项研究的目的是：1）使用客观和可靠的情感编码程序来注释来自癌症幸存者的大型文本语料库，2）将语言和心理特征结合到机器学习分类方法中，并识别这些特征中的哪些与由受过训练的人类评分员分配的代码最强相关，（3）发展心理学和自然语言处理相结合的方法来识别情绪应对行为的语言标记。为了实现这些目标，将从5个现有的语言数据集开发一个全面的充满情感的癌症通信语料库。五名评分员将被选中，并接受严格的训练程序，使用之前开发的情感编码系统编码情感表达。编码将使用基于互联网的编码界面进行，这将使研究者能够持续监测评估者之间的可靠性。在编码过程的同时，研究人员将把电子文本数据与关键的语言和心理特征联系起来，包括语言查询和单词计数（LIWC）、英语单词情感规范（ANEW）、WordNet、词性标签、大写和标点符号模式、表情符号和文本上下文。然后，使用自然语言处理工具的机器学习分类器将应用于文本/特征数据，并根据人类评级的情感代码进行验证。本研究的长期目标是提出一种客观识别应对行为，特别是情绪表达的方法，以补充自我报告措施，提高对慢性疾病，创伤或其他心理状况的适应的科学理解。这项工作对于确定癌症幸存者和其他人的心理社会干预措施的作用机制至关重要，对医学，心理学，计算语言学和人工智能领域具有重要意义。 公共卫生相关性：确定有助于调整癌症和其他慢性疾病的特定情感，认知和行为因素对于能够开发和改进有效的干预措施以促进健康和福祉至关重要。迄今为止，对这些因素作为作用机制的研究仅限于自我报告的措施，这些措施可能与其他更客观的指标没有很好的关联。这项拟议的研究将通过补充自我报告措施与客观识别的应对行为标记，如癌症患者使用的自然语言中的情感表达，提高我们识别行动机制的能力。

DESCRIPTION (provided by applicant): Understanding mechanisms of action is key to improving psychosocial interventions for cancer and other chronic disease conditions. In cancer, emotional expression has been identified as one possible mediator of the effect of psychosocial intervention on patient-reported outcomes. However, scientific evaluations of psychological mechanisms of adjustment to cancer and other chronic diseases are constrained by limitations associated with self-report measures. Because self-care resources, peer-to-peer networks, and more recent forms of psychosocial intervention are increasingly being delivered online, linguistic and behavioral data can be used to characterize internal coping processes, social interactions, and other manifest behaviors. Few tools are currently available for harnessing text as a potential data source, and signal detection indices of existing tools leave room for considerable improvement in these methodologies (Bantum & Owen, 2009). In the present study, natural language processing and other tools of computational linguistics will be used to develop a machine-learning classifier to identify emotional expression in electronic text data. The aims of the study are: 1) to annotate a large text corpus from cancer survivors using an objective and reliable emotion-coding procedure, 2) to incorporate linguistic and psychological features into a machine-learning classification method and identify which of these features are most strongly associated with codes assigned by trained human raters, and 3) to develop combined psychological and natural language processing (NLP) methods for identifying linguistic markers of emotional coping behaviors. To accomplish these aims, a comprehensive corpus of emotionally-laden cancer communications will be developed from 5 existing linguistic datasets. Five raters will be selected and undergo a rigorous training procedure for coding emotional expression using an emotion-coding system previously developed by the research. Coding will take place using an Internet-based coding interface that will allow the investigators to continuously monitor inter-rater reliability. Simultaneous with the coding process, the investigators will link the electronic text data with key linguistic and psychological features, including Linguistic Inquiry and Word Count (LIWC), Affective Norms for English Words (ANEW), WordNet, part of speech tags, patterns of capitalization and punctuation, emoticons, and textual context. A machine-learning classifier, using tools of natural language processing, will then be applied to the text/feature data and validated against human-rated emotion codes. The long-term objective of this research is to advance a methodology for objectively identifying coping behavior, particularly emotional expression, in order to supplement self-report measures and improve scientific understanding of adjustment to chronic disease, trauma, or other psychological conditions. This work is essential for identifying mechanisms of action in psychosocial interventions for cancer survivors and others and has significance for the fields of medicine, psychology, computational linguistics, and artificial intelligence. PUBLIC HEALTH RELEVANCE: Identifying specific emotional, cognitive, and behavioral factors that contribute to adjustment to cancer and other chronic diseases is essential for being able to develop and improve effective interventions to promote health and well-being. To date, the study of these factors as mechanisms of action has been limited to self-report measures that may not correlate well with other more objective indicators. The proposed study will improve our ability to identify mechanisms of action by supplementing self-report measures with objectively identified markers of coping behaviors such as emotional expression in natural language used by individuals living with cancer.