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Automated Mark Up of Affective Information in English Texts

机译:英文文本中的情感信息自动标记

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摘要

This paper presents an approach to automated marking up of texts with emotional labels. The approach considers in parallel two possible representations of emotions: as emotional categories and emotional dimensions. For each representation, a corpus of example texts previously annotated by human evaluators is mined for an initial assignment of emotional features to words. This results in a List of Emotional Words (LEW) which becomes a useful resource for later automated mark up. The proposed algorithm for automated mark up of text mirrors closely the steps taken during feature extraction, employing for the actual assignment of emotional features a combination of the LEW resource, the ANEW word list, and WordNet for knowledge-based expansion of words not occurring in either. The algorithm for automated mark up is tested and the results are discussed with respect to three main issues: relative adequacy of each one of the representations used, correctness and coverage of the proposed algorithm, and additional techniques and solutions that may be employed to improve the results.
机译:本文提出了一种自动标记带有情感标签的文本的方法。该方法同时考虑了两种可能的情绪表征:情绪类别和情绪维度。对于每种表示,将挖掘先前由人类评估者注释的示例文本的语料库,以将情感特征初始分配给单词。这将产生一个情感词列表(LEW),它成为以后自动标记的有用资源。提出的文本自动标记算法紧密反映了特征提取过程中采取的步骤,将情感特征,ANEW单词列表和WordNet结合起来用于情感特征的实际分配,从而实现了基于知识的单词扩展。要么。测试了自动标记算法,并针对三个主要问题讨论了结果:使用的每种表示形式的相对充分性,所提出算法的正确性和覆盖范围,以及可以用来改善广告质量的其他技术和解决方案结果。

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