Emotion detection in online social networks (OSNs) can benefit kinds of applications, such as personalized advertisement services, recommendation systems, etc. Conventionally, emotion analysis mainly focuses on the sentence level polarity prediction or single emotion label classification, however, ignoring the fact that emotions might coexist from users' perspective. To this end, in this work, we address the multiple emotions detection in OSNs from user-level view, and formulate this problem as a multilabel learning problem. First, we discover emotion labels correlations, social correlations, and temporal correlations from an annotated Twitter data set. Second, based on the above observations, we adopt a factor graph-based emotion recognition model to incorporate emotion labels correlations, social correlations, and temporal correlations into a general framework, and detect the multiple emotions based on the multilabel learning approach. Performance evaluation demonstrates that the factor graph-based emotion detection model can outperform the existing baselines.

中文翻译：

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在线社交网络中的情绪检测：多标签学习方法


在线社交网络（OSN）中的情绪检测可以使各种应用受益，例如个性化广告服务、推荐系统等。传统上，情绪分析主要关注句子级极性预测或单一情绪标签分类，然而，忽略了以下事实：从用户的角度来看，情绪可能是共存的。为此，在这项工作中，我们从用户层面的角度解决了 OSN 中的多种情绪检测，并将该问题表述为多标签学习问题。首先，我们从带注释的 Twitter 数据集中发现情感标签相关性、社会相关性和时间相关性。其次，基于上述观察，我们采用基于因子图的情绪识别模型，将情绪标签相关性、社会相关性和时间相关性纳入通用框架，并基于多标签学习方法检测多种情绪。性能评估表明，基于因子图的情绪检测模型可以优于现有的基线。