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Using lexical level information in discourse structures for Basque sentiment analysis

机译:使用词汇级别信息在话语结构中进行巴斯克语情绪分析

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Systems for opinion and sentiment analysis rely on different resources: a lexicon, annotated corpora and constraints (morphological, syntactic or discursive), depending on the nature of the language or text type. In this respect, Basque is a language with fewer linguistic resources and tools than other languages, like English or Spanish. The aim of this work is to study whether some kinds of discourse structures based on nuclearity are sufficient to correctly assign positive and negative polarity with a lexicon-based approach for sentiment analysis. The evaluation is performed in two phases: i) Text extraction following some constraints on discourse structure from manually annotated trees, ii) Automatic annotation of semantic orientation (or polarity). Results show that the method is useful to detect all positive cases, but fails with the negative ones. An error analysis shows that negative cases have to be addressed in a different way. The immediate results of this work include an evaluation on how discourse structure can be exploited in Basque. In the future, we will also publish a manually created Basque dictionary to use in sentiment analysis tasks.
机译:意见和情绪分析的系统依赖于不同的资源:根据语言或文本类型的性质,lexicon,注释的语料库和约束(形态,句法或话语)。在这方面,巴斯克是一种语言,具有比其他语言更少的语言资源和工具,如英语或西班牙语。这项工作的目的是研究基于核电力的某些类型的话语结构是否足以通过基于词汇的情绪分析来正确地分配正极和负极性。评估在两阶段进行:i)文本提取在手动注释的树木上的话语结构的一些限制之后,ii)自动注释语义定向(或极性)。结果表明,该方法可用于检测所有阳性案例,但与消极的方法失败。错误分析表明,必须以不同的方式解决负片情况。这项工作的直接结果包括关于如何在巴斯克中开采话语结构的评估。未来,我们还将发布手动创建的巴斯克文字典以用于情意分析任务。

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