In computational linguistics, binary sentiment analysis methods have been proposed to predict whether a document expresses a positive or a negative opinion. In this paper, we study a unique research problem—identifying environmental stimuli that contribute to different moods (mood triggers). Our analysis is enabled by an anonymous micro-journalling dataset, containing over 700,000 short journals from over 67,000 writers and their self-reported moods at the time of writing. We first build a multinomial logistic regression model to predict the mood (e.g., happy, sad, tired, productive) associated with a micro-journal. We then examine the model to identify predictive words and word trigrams associated with various moods. Our study offers new data-driven insights into public well-being.

中文翻译：

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微日志挖掘以了解情绪触发器

在计算语言学中，已经提出了二元情感分析方法来预测文档是表达正面意见还是负面意见。在本文中，我们研究了一个独特的研究问题——识别导致不同情绪（情绪触发器）的环境刺激。我们的分析是通过匿名微期刊数据集实现的，其中包含来自 67,000 多名作者的 700,000 多篇短期刊以及他们在写作时自我报告的情绪。我们首先建立了一个多项逻辑回归模型来预测与微型期刊相关的情绪（例如，快乐、悲伤、疲倦、富有成效）。然后我们检查模型以识别与各种情绪相关的预测词和词三元组。我们的研究为公众福祉提供了新的数据驱动见解。