Analysing Mood Patterns in the United Kingdom through Twitter Content
Analysing Mood Patterns in the United Kingdom through Twitter Content
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通过 Twitter 内容分析英国的情绪模式
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
N. Cristianini
中科院分区：
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作者：
Vasileios Lampos;Thomas Lansdall;R. Araya;N. Cristianini
社交媒体提供了大量由人们直接生成的地理位置和时间戳的文本内容。可以对这些信息进行分析，以深入了解大量用户的总体状况，并解决不同学科提出的科学问题。在这项工作中，我们通过使用Twitter消息中包含的情感负载词语来估计情绪变化的时间模式，可能反映了用户情绪中潜在的昼夜节律和季节性节奏。我们提出了一种使用情感词分类从文本中计算情绪分数的方法，并将其应用于英国夏季和冬季收集的数百万条推文。我们的分析结果为所有被调查的情绪类型检测到了强烈的和统计上显著的昼夜节律模式。季节性变化似乎没有记录到信号中的任何重要差异，但每种情绪类型在24小时内都存在周期性振荡。所有情绪的主要共同特征是上午10点左右的高峰期，但他们的情绪评分模式在晚上有所不同。
Social Media offer a vast amount of geo-located and time-stamped textual content directly generated by people. This information can be analysed to obtain insights about the general state of a large population of users and to address scientific questions from a diversity of disciplines. In this work, we estimate temporal patterns of mood variation through the use of emotionally loaded words contained in Twitter messages, possibly reflecting underlying circadian and seasonal rhythms in the mood of the users. We present a method for computing mood scores from text using affective word taxonomies, and apply it to millions of tweets collected in the United Kingdom during the seasons of summer and winter. Our analysis results in the detection of strong and statistically significant circadian patterns for all the investigated mood types. Seasonal variation does not seem to register any important divergence in the signals, but a periodic oscillation within a 24-hour period is identified for each mood type. The main common characteristic for all emotions is their mid-morning peak, however their mood score patterns differ in the evenings.