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首页> 外文期刊>International journal of synthetic emotions >Emotion Mining Using Semantic Similarity
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Emotion Mining Using Semantic Similarity

机译：利用语义相似度进行情感挖掘

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摘要

Social networks are considered as the most abundant sources of affective information for sentiment and emotion classification. Emotion classification is the challenging task of classifying emotions into different types. Emotions being universal, the automatic exploration of emotion is considered as a difficult task to perform. A lot of the research is being conducted in the field of automatic emotion detection in textual data streams. However, very little attention is paid towards capturing semantic features of the text. In this article, the authors present the technique of semantic relatedness for automatic classification of emotion in the text using distributional semantic models. This approach uses semantic similarity for measuring the coherence between the two emotionally related entities. Before classification, data is pre-processed to remove the irrelevant fields and inconsistencies and to improve the performance. The proposed approach achieved the accuracy of 71.795%, which is competitive considering as no training or annotation of data is done.
机译：社交网络被认为是情感和情感分类中情感信息的最丰富来源。情感分类是将情感分为不同类型的一项艰巨任务。情感是普遍的，情感的自动探索被认为是一项艰巨的任务。在文本数据流中的自动情感检测领域中正在进行许多研究。但是，对于捕获文本的语义特征却很少关注。在本文中，作者介绍了使用分布语义模型对文本中的情感进行自动分类的语义相关性技术。这种方法使用语义相似性来测量两个情感相关实体之间的连贯性。在分类之前，对数据进行预处理以消除不相关的字段和不一致之处并提高性能。所提出的方法达到了71.795％的准确度，考虑到没有进行数据的训练或注释，因此具有竞争力。

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