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Sentiment Analysis of Online Product Reviews with Semi-supervised Topic Sentiment Mixture Model

机译：半监督主题情感混合模型的在线产品评论的情感分析

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Analysis the positive and negative sentiments about each topic of the product are very useful to the customers and manufacturers. In this paper we propose a new topic sentiment mixture model which we call Semi-supervised Co-LDA model to obtain the positive and negative opinions from the reviews about each product. The Semi-supervised Co-LDA can model the topic and sentiment of the product reviews simultaneously. The Semi-supervised Co-LDA model we proposed is a semi-supervised model, which utilizes the well-written expert reviews as labeled data. The Co-LDA model has an additional advantage that can integrate expert opinions and ordinary opinions. Empirical experiments on the online reviews datasets from CNET show that this approach is effective for topic sentiment analysis of the product. The Co-LDA model is quite general, which can be applied to many fields such as modeling opinions in weblogs, user behavior prediction.
机译：分析产品各主题的积极和消极情绪对客户和制造商非常有用。在本文中，我们提出了一个新的主题情绪混合模型，我们呼叫半监督的CO-LDA模型，以获得关于每个产品的评论的积极和负面意见。半监督的Co-LDA可以同时模拟产品评论的主题和情绪。我们提出的半监督CO-LDA模型是一个半监督模型，它利用书面良好的专家评论作为标记数据。 CO-LDA模型具有额外的优势，可以整合专家意见和普通意见。在线评论来自CNET的数据集的实证实验表明，这种方法对于产品的主题情感分析是有效的。 CO-LDA模型非常一般，可以应用于许多领域，例如博客中的博客中的意见，用户行为预测。

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