Sentiment analysis in various languages has been a hot research topic with several applications. Most of the existing models have been reported to work well with widely used language. Were the lass directly applying these models to poor-quality corpora often leads to low results. Thus, to deal withthese shortcoming we propose a cross-lingual sentiment topic model evolution over time (CLSTOT) which jointly models time with topic and sentiment. In CLSTOT, we consider the mapping between sentiment-aware topics under different cultures and analyze their evolution over time. The topic-specific sentiment is extracted using the entire data and not for each single document. As long as providing sentiment-topic, we can predict the timestamps for each test document by finding its most likely location over the timeline. This is achieved by using inference algorithm which is based on Gibbs Sampling. The experimental results on Chinese and English newsreader dataset; Chinese from SinaNews2, and English from Yahoo1, show that CLSTOT achieves significant improvement over the state-of-the-art.

中文翻译：

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跨语言情感主题模型随时间的演变

各种语言的情感分析已成为具有多个应用程序的热门研究主题。据报道，大多数现有模型都可以与广泛使用的语言很好地配合使用。如果小众直接将这些模型应用于质量低下的语料库，通常会导致较低的结果。因此，为了解决这些缺点，我们提出了跨语言的情感主题模型随时间的演变（CLSTOT），该模型共同对带有主题和情感的时间进行建模。在CLSTOT中，我们考虑了不同文化下的意识感知主题之间的映射，并分析了它们随着时间的演变。主题特定的情感是使用整个数据而不是每个文档提取的。只要提供情感主题，我们就可以通过在时间轴上找到每个文档的最可能位置来预测每个文档的时间戳。这是通过使用基于Gibbs采样的推理算法来实现的。中英文新闻阅读器数据集的实验结果；来自SinaNews2的中文和来自Yahoo1的英文显示，CLSTOT与最新技术相比取得了显着进步。