Ontology-Based Approach to Social Data Sentiment Analysis: Detection of Adolescent Depression Signals
Ontology-Based Approach to Social Data Sentiment Analysis: Detection of Adolescent Depression Signals
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DOI：
10.2196/jmir.7452
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发表时间：
2017-07-01
影响因子：
7.4
通讯作者：
Song, Tae-Min
中科院分区：
文献类型：
--
作者：
Jung, Hyesil;Park, Hyeoun-Ae;Song, Tae-Min
背景：社交网络服务（SNS）包含大量关于青少年的感受、想法、兴趣和行为模式的信息，可以通过分析 SNS 帖子来获得。表达特定领域中共享概念及其关系的本体可以用作社交媒体数据分析的语义框架。目的：本研究的目的是完善青少年抑郁症本体和术语，作为分析社交媒体数据的框架，并评估类别之间的描述逻辑以及该本体在情感分析中的适用性。方法：使用能力问题定义本体的领域和范围。构成本体论和术语的概念是从有关青少年抑郁症的临床实践指南、文献和社交媒体帖子中收集的。定义了类概念、它们的层次结构以及类概念之间的关系。使用实体-属性-值（EAV）三元组数据模型设计本体的内部结构，并将本体的超类与上层本体对齐。通过将从常见问题 (FAQ) 的答案中提取的概念映射到从描述逻辑查询派生的本体概念上来评估类之间的描述逻辑。通过使用本体 EAV 模型检查 1358 个情感短语的可表示性并使用本体类概念对社交媒体数据进行情感分析，验证了本体的适用性。 结果：我们开发了一个青少年抑郁症本体，包含 443 个类和 60 个类之间的关系；该术语包括 443 个类别的 1682 个同义词。在描述逻辑测试中，没有发现类之间的关系有错误，常见问题解答中引用的概念大约有89%（55/62）映射到了本体类上。在适用性方面，本体类的 EAV 三元组模型代表了情感词典中约 91.4% 的情感短语。在情绪分析中，“学业压力”和“自杀”对青少年抑郁情绪产生负面影响。结论：本研究开发的本体论和术语为分析青少年抑郁症的社交媒体数据提供了语义基础。为了在社交媒体数据分析中有用，本体，尤其是术语，需要不断更新，以反映青少年在社交媒体帖子中使用的快速变化的术语。此外，应该在本体中添加更多反映抑郁相关情绪的属性和值集。
Background: Social networking services (SNSs) contain abundant information about the feelings, thoughts, interests, and patterns of behavior of adolescents that can be obtained by analyzing SNS postings. An ontology that expresses the shared concepts and their relationships in a specific field could be used as a semantic framework for social media data analytics.Objective: The aim of this study was to refine an adolescent depression ontology and terminology as a framework for analyzing social media data and to evaluate description logics between classes and the applicability of this ontology to sentiment analysis.Methods: The domain and scope of the ontology were defined using competency questions. The concepts constituting the ontology and terminology were collected from clinical practice guidelines, the literature, and social media postings on adolescent depression. Class concepts, their hierarchy, and the relationships among class concepts were defined. An internal structure of the ontology was designed using the entity-attribute-value (EAV) triplet data model, and superclasses of the ontology were aligned with the upper ontology. Description logics between classes were evaluated by mapping concepts extracted from the answers to frequently asked questions (FAQs) onto the ontology concepts derived from description logic queries. The applicability of the ontology was validated by examining the representability of 1358 sentiment phrases using the ontology EAV model and conducting sentiment analyses of social media data using ontology class concepts.Results: We developed an adolescent depression ontology that comprised 443 classes and 60 relationships among the classes; the terminology comprised 1682 synonyms of the 443 classes. In the description logics test, no error in relationships between classes was found, and about 89% (55/62) of the concepts cited in the answers to FAQs mapped onto the ontology class. Regarding applicability, the EAV triplet models of the ontology class represented about 91.4% of the sentiment phrases included in the sentiment dictionary. In the sentiment analyses, "academic stresses" and "suicide" contributed negatively to the sentiment of adolescent depression.Conclusions: The ontology and terminology developed in this study provide a semantic foundation for analyzing social media data on adolescent depression. To be useful in social media data analysis, the ontology, especially the terminology, needs to be updated constantly to reflect rapidly changing terms used by adolescents in social media postings. In addition, more attributes and value sets reflecting depression-related sentiments should be added to the ontology.