A Systematic Spatial and Temporal Sentiment Analysis on Geo-Tweets
A Systematic Spatial and Temporal Sentiment Analysis on Geo-Tweets
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DOI：
10.1109/access.2019.2961100
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发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
Clunis, Julaine
中科院分区：
文献类型：
--
作者：
Hu, Tao;She, Bing;Clunis, Julaine
情绪影响着人们生活的方方面面，对心理健康有很大影响。本文探讨了从 2016 年 1 月至 12 月地理推文数据中提取的本地用户情绪，并从空间和时间角度进行分析。由于大量的噪声数据和提取本地用户的复杂过程，创建了一个工作流程，方便更多的研究人员使用类似的地理推文数据集重现、复制或扩展该过程。工作流程正在Harvard Dataverse 共享（https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/6N9VUF）。使用处理后的数据，根据内容对每条推文的情绪进行分类。然后，在每月、每天和每小时的水平上分析积极情绪、神经情绪和消极情绪总数的总体时间变化。从空间角度来看，采用局部空间关联指标（LISA）统计方法来发现空间聚类。为了探索积极情绪的内容，本文应用潜在狄利克雷分配（LDA）模型将具有积极情绪的地理推文分类为不同的主题。将地理空间信息与主题相结合，发现了一些模式，这些模式展示了 Twitter 内容的性质与地点和用户的特征之间的关联。例如，周末活动以及朋友和家人聚会是用户更喜欢发布积极推文的时间。与美国其他地区相比，美国西部地区的用户倾向于在 Twitter 上发布更多照片来分享精彩时刻。
Sentiment affects every aspect of people's lives and has strong impact on their mental health. This paper explores local users' sentiments extracted from Geo-tweets data from January to December 2016, analyzed in the spatial and temporal perspective. Because of large amount of noisy data and complicated procedure of extracting local user, a workflow is created, facilitating more researchers to reproduce, replicate or extend the procedures using similar Geo-tweet dataset. The workflow is sharing at Harvard Dataverse (https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/6N9VUF). Using the processed data, each tweet's sentiment is classified according to the content. Then, the overall temporal variations of total number of positive, neural, and negative sentiments are analyzed on a monthly, daily and hourly level. From a spatial perspective, the Local Indicators of Spatial Association (LISA) statistical method is employed to discover the spatial clusters. In order to explore the content of positive sentiments, this paper applies the Latent Dirichlet Allocation (LDA) model to classify the Geo-tweets with positive sentiments into different topics. Combining the geospatial information with the topics, some patterns are found which demonstrate the associations between the nature of Twitter content and the characteristics of places and users. For example, weekend events and friend and family gatherings are the time that users prefer to post positive tweets. In the western part of US, users tend to post more photos to share the great moment on Twitter than other parts of the US.