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Collaborative Inference of Sentiments from Texts

机译:从文本中共同推断情感

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Sentiment analysis deals with inferring people's sentiments and opinions from texts. An important aspect of sentiment analysis is polarity classification, which consists of inferring a document's polarity - the overall sentiment conveyed by the text - in the form of a numerical rating. In contrast to existing approaches to polarity classification, we propose to take the authors of the documents into account. Specifically, we present a nearest-neighbour collaborative approach that utilises novel models of user similarity. Our evaluation shows that our approach improves on state-of-the-art performance, and yields insights regarding datasets for which such an improvement is achievable.
机译:情感分析用于从文本中推断出人们的情感和观点。情感分析的一个重要方面是极性分类,它包括以数字等级的形式推断文档的极性-文本传达的总体情感。与极性分类的现有方法相反,我们建议考虑文档的作者。具体来说,我们提出了一种利用用户相似性的新颖模型的最近邻居协作方法。我们的评估表明,我们的方法改进了最先进的性能,并获得了有关可以实现这种改进的数据集的见解。

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