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
Ontology based social data emotion analysis method ： Detection of depression signals in adolescents

Keywords:
ontology noumenon
adolescent teenagers
depression depression
data mining data mining
social media data Social media data

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.
background ： Social networking services （SNS） It contains a lot of emotions about teenagers 、 thought 、 Information about interests and behavior patterns , This information can be analyzed SNS Post get . An ontology that expresses shared concepts and their relationships in a specific domain can be used as a semantic framework for social media data analysis .

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.
Purpose ： The purpose of this study is to refine adolescent depression ontology and The term , As a framework for analyzing social media data , Evaluate the description logic between classes and the applicability of the ontology in emotion 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.
Method ： The domain and scope of ontology are defined by using the ability problem . The concepts that make up ontology and terminology come from clinical practice guidelines 、 Literature and social media posts about adolescent depression . Defines the concept of class 、 Their hierarchy and the relationship between class concepts . Use entity attribute values （EAV） The triple data model designs the internal structure of ontology , And the superclass of ontology is aligned with the upper ontology . The description logic between classes will be passed from FAQ (FAQs) The concepts extracted in are mapped to the ontology concepts obtained from the description logic query , To evaluate the description logic between classes . By using ontology EAV Model for 1358 Test the representativeness of Emotional Phrases , And use the concept of ontology to analyze the emotion of social media data , The applicability of ontology is verified .

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.
result ： We have established the ontology of adolescent depression , Include 443 Classes and 60 The relationship between classes ; The term is used by 443 Class purposes 1682 A synonym consists of . In describing logic tests , No errors were found in the relationship between classes , And there are 89%（55/62） Of FAQ The concepts referenced in the answer map to ontology classes . In terms of applicability , Ontological EAV The triple model accounts for about% of the Emotional Phrases in the emotional dictionary 91.4%. In emotional analysis , Academic stress and suicide have a negative impact on adolescents' 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.