The significant rise in suicides is a major cause of concern in public health domain. Depression plays a major role in increasing suicide ideation among the individuals. Although most of the suicides can be avoided with prompt intercession and early diagnosis, it has been a serious challenge to detect the at-risk individuals. Our current work focuses on learning three closely related tasks, viz. depression detection, sentiment citation, and to investigate their impact in analysing the mental state of the victims. We extend the existing standard emotion annotated corpus of suicide notes in English, CEASE, with additional 2539 sentences collected from 120 new notes. We annotate the consolidated corpus with appropriate depression labels and multi-label emotion classes. We further leverage weak supervision to annotate the corpus with sentiment labels. We propose a deep multitask framework that features a knowledge module that uses SenticNet’s IsaCore and AffectiveSpace vector-spaces to infuse external knowledge specific features into the learning process. The system models emotion recognition (the primary task), depression detection and sentiment classification (the secondary tasks) simultaneously. Experiments show that our proposed multitask system obtains the highest cross-validation MR of 56.47 %. Evaluation results show that all our multitask models perform better than their single-task variants indicating that the secondary tasks (depression detection and sentiment classification) improve the performance of the primary task (emotion recognition) when all tasks are learned jointly.

自杀的大量增加是公共卫生领域关注的一个主要原因。抑郁症在增加个体的自杀观念中起主要作用。尽管可以通过及时的代祷和早期诊断来避免大多数自杀，但是发现高危个体仍然是一个严峻的挑战。我们当前的工作重点是学习三个紧密相关的任务，即。抑郁症检测，情绪引用，以及调查其在分析受害者心理状态方面的影响。我们以英语（CEASE）扩展了现有的标准自杀式情感注释语料库，并从120个新笔记中收集了另外2539个句子。我们用适当的抑郁标签和多标签情感类别注释合并的语料库。我们进一步利用薄弱的监督来为语料库添加情感标签。我们提出了一个深层的多任务框架，该框架具有一个使用SenticNet的IsaCore和AffectiveSpace的知识模块。向量空间，将外部知识的特定特征注入学习过程。该系统同时对情绪识别（主要任务），抑郁症检测和情绪分类（次要任务）建模。实验表明，我们提出的多任务系统获得了最高的交叉验证MR，为56.47％。评估结果表明，我们所有的多任务模型均比其单任务变体表现更好，表明当共同学习所有任务时，次要任务（抑郁检测和情绪分类）可改善主要任务的性能（情感识别）。