Social Media Big Data Based Research On Semantics Of Inferiority Complex And Its Spatio-temporal Evolution

With the development of internet,large database,cloud computing and artificial intelligence,the Internet Plus Strategy stimulates the development of social media,where each user can write articles,share ideas and express emotion as well as interact with others anonymously.Nowadays,as a network society,social media with the rapid increase of users and updated data,has provided people with a new view to observe the world.The scholars even utilize the social media to research diseases,such as aids,and show that some psychological problems can be revealed by social media.Self-abasement is a complicated emotional system with self-doubt,weakness and the lack of self-esteem and self-help,which is regarded as character defect psychologically.Self-abased people are usually depressed and unsociable.Without the timely detection and correct guidance,they will suffer from inferiority emotion for a long time and decline their physical function and immunity,which could lead to various diseases.However,the undetected and untreated rates are high for patients,because self-abasement is a subconscious emotion and self-abased people could not realize their inferiority emotion.Besides,because the questionnaire survey is used widely in traditional research method and treatment,it is likely for the informants or patients to full in the questionnaire truly with the worry about privacy leakage,which is difficult for scholars to have deeper research on inferiority complex.Due to the openness and anonymity of the social media,self-abased people can express emotion,reveal their own experience and seek for help online,which supplies a large quantity of data for this research.According to what has been mentioned above,it analyzes the related research character,hotspots and trend on self-abasement based on the Scientometrics,as the theoretical basis for the paper on the study of inferiority complex in social media.From the perspective of social media,it established a semantic model for the research on semantics of inferiority complex,and utilized GIS spatial model to analyze semantics of inferiority complex.Finally,with the utilization of dimension deduction and spatial data gridding method,the correlation expression of driving factor about inferiority complex can be delivered in the spatial unit.The paper mainly studies from following these aspects.(1)With the utilization of scientific metrology method and visualization method,the paper discusses on social media,health care as well as the related characteristics and trend of self-abasement from the macro and micro views.The research shows that it is essential and feasible to use social media to study on inferiority complex.Also,the research consequences provide the researchers who are new to study in this field with a comprehensive visualized scientific knowledge structure.(2)With the utilization of Natural Language Processing technology and combination of social media data characteristics,the semantic model of inferiority complex will be set up dynamically based on social media data.Besides,through threshold clustering method,the semantic primitive synonymy will be clustered.The semantic primitives of the self-abasement will be extracted by the algorithm of correlated semantic primitive extraction and the visualized expression and analysis of semantic primitives will be realized by the algorithm of manifold learning dimension reduction.(3)The changing process of the subject of self-abasement will be reflected to the four semantic spaces after the binary classification.Then,the semantic characteristics will be analyzed by semantic map.In order to analyze the mode of some specific semantic primitives influenced by other semantics and provide the innovative view about the method of social media semantic analysis,GIS spatial autocorrelation analysis method will be introduced to the study on social media semantics and discover semantic primitive spatial clustering characteristics.(4)Taking the economy,society,education and various POI factors in cities into the consideration,with the utilization of dimension deduction,spatial data gridding method and other methods,the correlation expression of factors above in the spatial unit will be come true.Through the geographical weighted regression model,the authors research the influence of economy,society,and education on self-abasement,and discuss the spatiotemporal variation and evolution of self-abasement in cities based on spatial gridding unit.