Day 4.Social Data Sentiment Analysis: Detection of Adolescent Depression Signals

Title:
Ontology-Based Approach to Social Data Sentiment Analysis: Detection of Adolescent Depression Signals
基于本体的社会数据情感分析方法：青少年抑郁信号的检测

Keywords:
ontology 本体
adolescent 青少年
depression 抑郁症
data mining 数据挖掘
social media data 社交媒体数据

Abstract：
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.
背景：社交网络服务（SNS）包含了大量关于青少年的情感、思想、兴趣和行为模式的信息，这些信息可以通过分析SNS帖子获得。一个表达特定领域中共享概念及其关系的本体可以作为社交媒体数据分析的语义框架。

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.
目的：本研究的目的是提炼青少年抑郁本体和术语，作为分析社交媒体数据的框架，评估类之间的描述逻辑以及该本体在情绪分析中的适用性。

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.
方法：利用能力问题定义了本体的领域和范围。构成本体论和术语的概念来自临床实践指南、文献和关于青少年抑郁症的社交媒体帖子。定义了类概念、它们的层次结构以及类概念之间的关系。利用实体属性值（EAV）三元组数据模型设计了本体的内部结构，并将本体的超类与上层本体进行了对齐。类之间的描述逻辑通过将从常见问题解答(FAQs) 中提取的概念映射到描述逻辑查询得到的本体概念上，来评估类之间的描述逻辑。通过使用本体EAV模型对1358个情感短语进行可代表性检验，并利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。

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.
结果：我们建立了青少年抑郁本体论，包括443个班级和60个班级之间的关系；该术语由443个类目的1682个同义词组成。在描述逻辑测试中，没有发现类之间的关系有错误，并且有89%（55/62）的FAQ答案中引用的概念映射到本体类上。在适用性方面，本体类的EAV三元组模型约占情感词典中情感短语的91.4%。在情绪分析中，学业压力和自杀对青少年抑郁情绪有负向影响。

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.