Systematical Approach for Detecting the Intention and Intensity of Feelings on Social Network

被引：8

作者：

Tai, Chih-Hua ^{[1
]}

Tan, Zheng-Han ^{[1
]}

Chang, Yue-Shan ^{[1
]}

机构：

[1] Natl Taipei Univ, Dept Comp Sci & Informat Engn, New Taipei 23741, Taiwan

来源：

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS | 2016年 / 20卷 / 04期

关键词：

Feeling intensity; latent Dirichlet allocation (LDA); supervised LDA (sLDA); DEPRESSION; DIAGNOSIS;

D O I：

10.1109/JBHI.2016.2535721

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Online posts not only represent the records of people's lives but also reveal their satisfaction with life and relationships as well as potential mental illnesses. The detection of (strong or general) negative as well as (strong or general) positive feelings of people from online posts can keep us from carelessly missing their important moments, difficult or great, due to the overloaded information in the daily life and lead to a better society. Therefore, in this paper, we build a Feeling Distinguisher system based on supervised Latent Dirichlet Allocation (sLDA), Latent Dirichlet Allocation, and SentiWordNet methodologies for detecting a person's intention and intensity of feelings through the analysis of his/her online posts. Experimental results on posts collected from five social network websites demonstrate the effectiveness of FeD. The performance of FeD is about 1.08-1.18 folds that of SVM and sLDA.

引用

页码：987 / 995

页数：9

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