ARSA: a sentiment-aware model for predicting sales performance using blogs
ARSA: a sentiment-aware model for predicting sales performance using blogs
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DOI：
10.1145/1277741.1277845
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发表时间：
2007-07
期刊：
影响因子：
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通讯作者：
Yang Liu;Xiangji Huang;Aijun An;Xiaohui Yu
中科院分区：
文献类型：
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作者：
Yang Liu;Xiangji Huang;Aijun An;Xiaohui Yu
由于它的高度普及，博客（或博客）提供了丰富的信息，可以非常有助于评估公众的情绪和意见。在本文中，我们研究了从博客中挖掘情感信息的问题，并研究了如何使用这些信息来预测产品的销售业绩。基于情感的复杂性的分析，我们提出了情感PLSA（S-PLSA），其中一个博客条目被看作是一个文件所产生的一些隐藏的情感因素。在博客数据上训练S-PLSA模型使我们能够获得嵌入在博客中的情感信息的简洁摘要。然后，我们提出ARSA，一个自回归情感感知模型，利用S-PLSA捕获的情感信息来预测产品的销售业绩。在一个电影数据集上进行了大量的实验。我们将ARSA与不考虑情感信息的替代模型以及具有不同特征选择方法的模型进行比较。实验证明了该方法的有效性和优越性。
Due to its high popularity, Weblogs (or blogs in short) present a wealth of information that can be very helpful in assessing the general public's sentiments and opinions. In this paper, we study the problem of mining sentiment information from blogs and investigate ways to use such information for predicting product sales performance. Based on an analysis of the complex nature of sentiments, we propose Sentiment PLSA (S-PLSA), in which a blog entry is viewed as a document generated by a number of hidden sentiment factors. Training an S-PLSA model on the blog data enables us to obtain a succinct summary of the sentiment information embedded in the blogs. We then present ARSA, an autoregressive sentiment-aware model, to utilize the sentiment information captured by S-PLSA for predicting product sales performance. Extensive experiments were conducted on a movie data set. We compare ARSA with alternative models that do not take into account the sentiment information, as well as a model with a different feature selection method. Experiments confirm the effectiveness and superiority of the proposed approach.