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Adjective Intensity and Sentiment Analysis

机译:形容词强度和情感分析

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For fine-grained sentiment analysis, we need to go beyond zero-one polarity and find a way to compare adjectives that share a common semantic property. In this paper, we present a semi-supervised approach to assign intensity levels to adjectives, viz. high, medium and low, where adjectives are compared when they belong to the same semantic category. For example, in the semantic category of EXPERTISE, expert, experienced and familiar are respectively of level high, medium and low. We obtain an overall accuracy of 77% for intensity assignment. We show the significance of considering intensity information of adjectives in predicting star-rating of reviews. Our intensity based prediction system results in an accuracy of 59% for a 5-star rated movie review corpus.
机译:对于细粒度的情感分析,我们需要超越零一极性,并找到一种比较具有共同语义属性的形容词的方法。在本文中,我们提出了一种半监督的方法来将强度级别分配给形容词,即。高,中和低,当形容词属于同一语义类别时,对它们进行比较。例如,在EXPERTISE的语义类别中,专家,经验丰富和熟悉的级别分别为高,中和低。对于强度分配,我们获得了77%的总体精度。我们展示了在预测评论的星级时考虑形容词的强度信息的重要性。我们基于强度的预测系统可得出5星级电影评论语料库的准确度为59%。

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