A Classification of Adjectives for Polarity Lexicons Enhancement
A Classification of Adjectives for Polarity Lexicons Enhancement
复制标题
用于增强极性词典的形容词分类
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Núria Bel
中科院分区：
文献类型：
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作者：
Silvia Vázquez;Núria Bel
主观语言检测是情感分析中最重要的挑战之一。由于在固执己见的文本中的权重和频率，形容词被认为是意见提取过程中的关键部分。这些主观单位越来越频繁地被收集在极性词典中，在这些词典中，它们出现在注释中。然而，目前，任何极性词典都考虑了跨域的先前极性变化。本文证明了大多数形容词的先验极性值会随论域的不同而变化。我们提出了一个领域依赖和领域独立的形容词之间的区别。此外，我们的分析导致我们提出了一个进一步的分类与主观程度：恒定，混合和高度主观形容词。在这种分类之后，极性值将更好地支持情绪分析。
Subjective language detection is one of the most important challenges in Sentiment Analysis. Because of the weight and frequency in opinionated texts, adjectives are considered a key piece in the opinion extraction process. These subjective units are more and more frequently collected in polarity lexicons in which they appear annotated with their prior polarity. However, at the moment, any polarity lexicon takes into account prior polarity variations across domains. This paper proves that a majority of adjectives change their prior polarity value depending on the domain. We propose a distinction between domain dependent and domain independent adjectives. Moreover, our analysis led us to propose a further classification related to subjectivity degree: constant, mixed and highly subjective adjectives. Following this classification, polarity values will be a better support for Sentiment Analysis.