Dynamic joint sentiment-topic model
Dynamic joint sentiment-topic model
复制标题
动态联合情感主题模型
DOI：
10.1145/2542182.2542188
复制
发表时间：
2014
影响因子：
5
通讯作者：
He Y
中科院分区：
文献类型：
--
作者：
He Y
社交媒体数据是由大量不受控制的用户持续产生的。这种数据的动态性质要求情感和主题分析模型也动态更新，捕获文本中情感和主题的最新语言使用。我们提出了一个动态的联合情感主题模型（dJST），它允许检测和跟踪当前和经常性的兴趣和主题和情感的变化的意见。主题和情感动态都是通过假设当前情感主题特定的词分布是根据先前时期的词分布生成的来捕获的。我们研究了三种不同的方法来解释这种依赖信息：（1）滑动窗口，其中当前的情感主题词分布依赖于最近Sepochs中的先前的情感主题词分布：（2）跳过模型，其中历史情感主题词分布通过跳过其间的一些时期来考虑;（3）多尺度模型，其中考虑了以前的长、短时间尺度分布。我们推导出有效的在线推理程序，以顺序更新模型与新到达的数据，并显示我们提出的模型的有效性Mozilla附加评论爬在2007年和2011年之间。
Social media data are produced continuously by a large and uncontrolled number of users. The dynamic nature of such data requires the sentiment and topic analysis model to be also dynamically updated, capturing the most recent language use of sentiments and topics in text. We propose a dynamic Joint Sentiment-Topic model (dJST) which allows the detection and tracking of views of current and recurrent interests and shifts in topic and sentiment. Both topic and sentiment dynamics are captured by assuming that the current sentiment-topic-specific word distributions are generated according to the word distributions at previous epochs. We study three different ways of accounting for such dependency information: (1)sliding windowwhere the current sentiment-topic word distributions are dependent on the previous sentiment-topic-specific word distributions in the lastSepochs; (2)skip modelwhere history sentiment topic word distributions are considered by skipping some epochs in between; and (3)multiscale modelwhere previous long- and short- timescale distributions are taken into consideration. We derive efficient online inference procedures to sequentially update the model with newly arrived data and show the effectiveness of our proposed model on the Mozilla add-on reviews crawled between 2007 and 2011.