Detecting Adolescent Psychological Pressures from Micro-Blog
Detecting Adolescent Psychological Pressures from Micro-Blog
复制标题
DOI：
10.1007/978-3-319-06269-3_10
复制
发表时间：
2014-04
期刊：
影响因子：
--
通讯作者：
Yuanyuan Xue;Qi Li;Li Jin;Ling Feng;D. Clifton;G. Clifford
中科院分区：
文献类型：
--
作者：
Yuanyuan Xue;Qi Li;Li Jin;Ling Feng;D. Clifton;G. Clifford
青少年正经历着来自学习、交流、情感和自我认知的不同心理压力。如果这些心理压力不能得到妥善解决，就会转化为心理问题，可能会导致严重的后果。传统的面对面心理诊疗由于缺乏时效性和多样性，不能完全满足缓解青少年压力的需求。随着微博成为青少年获取信息、互动、自我表达、情感释放的主流媒体渠道，我们设想一个微博平台，通过青少年的推文感知心理压力，帮助青少年通过微博释放压力。我们研究了一些可能从他们的推文中揭示青少年压力的特征，然后测试了五种分类器（朴素贝叶斯、支持向量机、人工神经网络、随机森林和高斯过程分类器）用于压力检测。我们还提出了在时间序列中聚合基于单推文的检测结果的方法，以概述青少年在一段时间内的压力波动。实验结果表明，高斯过程分类器提供了最高的检测精度，因为它在存在很大程度的不确定性的情况下具有鲁棒性，这些不确定性可能会遇到以前未见过的推文训练数据。其中，推文的情绪程度结合了负面情绪词汇、表情符号、感叹号和问号，在心理压力检测中起着首要作用。
Adolescents are experiencing different psychological pressures coming from study, communication, affection, and self-recognition. If these psychological pressures cannot properly be resolved, it will turn to mental problems, which might lead to serious consequences. Traditional face-to-face psychological diagnosis and treatment cannot meet the demand of relieving teenagers’ stress completely due to its lack of timeliness and diversity. With micro-blog becoming a popular media channel for teenagers’ information acquisition, interaction, self-expression, emotion release, we envision a micro-blog platform to sense psychological pressures through teenagers’ tweets, and assist teenagers to release their stress through micro-blog. We investigate a number of features that may reveal teenagers’ pressures from their tweets, and then test five classifiers (Naive Bayes, Support Vector Machines, Artificial Neural Network, Random Forest, and Gaussian Process Classifier) for pressure detection. We also present ways to aggregate single-tweet based detection results in time series to overview teenagers’ stress fluctuation over a period of time. Experimental results show that the Gaussian Process Classifier offers the highest detection accuracy due to its robustness in the presence of a large degree of uncertainty that may be encountered with previously-unseen training data on tweets. Among the features, tweet’s emotional degree combining negative emotional words, emoticons, exclamation and question marks, plays a primary role in psychological pressure detection.