基于文本的抑郁情感倾向识别模型

Depression Tendency Identification Model Based on Text Content Analysis

针对学生在新浪微博文本中所表现出来的抑郁情感倾向,提出了一种识别抑郁情感倾向的模型.通过在本校广泛发动学生在线填写抑郁自评量表,获得学生的量表得分.采集学生的微博文本,并请本校心理学老师对微博进行人工标注.在预处理阶段,利用抑郁情感词典重新组合在分词阶段被拆分的抑郁情感词,以提高识别正确率.然后基于支持向量机构建一个情感分类器对微博数据进行训练,经过不断的学习反馈,获得较好的分类效果;最后,定义了抑郁指数来衡量个体在一段时间内的抑郁倾向程度.实验结果表明,抑郁指数衡量的抑郁程度大致与量表结果吻合,该方法识别准确率达到82.35%.

In order to solve the problem of identifying depression tendency among students on sina microblog platform, this paper proposes a depression tendency identification model. By inviting students widely to fill in the self-rating depression scale online on campus we can get the students＇ score. We collect students＇ microblog text and ask the psychology teacher to annotate the microblog artificially. In the pretreatment stage, we use the depression emotional dictionary to reassemble the depressed emotion words that are split at the segmentation stage so as to improve the recognition accuracy rate. And then we build a classifier based on the support vector machine to train the data. Through continuous learning and feedback, we get a better classification result. Finally, this paper defines the depression index and uses it to measure the degree of depression for a period of time. The experimental results indicate that the degree of depression measured by depression index is approximately consistent with the results of the scale, the accuracy of the method being 82.35%.