Textual emotion detection has a high impact on business, society, politics or education with applications such as, detecting depression or personality traits, suicide prevention or identifying cases of cyber-bulling. Given this context, the objective of our research is to contribute to the improvement of emotion recognition task through an automatic technique focused on reducing both the time and cost needed to develop emotion corpora. Our proposal is to exploit a bootstrapping approach based on intensional learning for automatic annotations with two main steps: 1) an initial similarity-based categorization where a set of seed sentences is created and extended by distributional semantic similarity (word vectors or word embeddings); 2) train a supervised classifier on the initially categorized set. The technique proposed allows us an efficient annotation of a large amount of emotion data with standards of reliability according to the evaluation results.

中文翻译：

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有效建立自动注释情感语料库的意图学习

文本情感检测对商业、社会、政治或教育具有重大影响，其应用包括检测抑郁症或人格特征、预防自杀或识别网络欺凌案例。鉴于这种情况，我们研究的目标是通过一种专注于减少开发情感语料库所需的时间和成本的自动技术，为改进情感识别任务做出贡献。我们的提议是利用基于内涵学习的自举方法进行自动注释，主要有两个步骤：1）初始基于相似性的分类，其中通过分布语义相似性（词向量或词嵌入）创建和扩展一组种子句子；2）在最初分类的集合上训练一个监督分类器。