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Indonesian Affective Word Resources Construction in Valence and Arousal Dimension for Sentiment Analysis

机译:基于情感和价域的印尼情感词资源建设的情感分析

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Research in the field of text analysis will always be related to words, either the selection of words to be used or the position of the words in a sentence. Furthermore, a hypothesis that each language difference can cause different meanings, makes some researchers interested in doing research classifying words based on emotion or affective words. Research focuses on affective states as a continuous numerical value to the dimensions of valence and arousal. Sentiment analysis that is usually done with positive and negative category approaches, nowadays, the dimensional approach can provide more analysis of grained sentiments. On the other hand, the affective words dataset with valence and arousal rating are still very rare, especially for the Indonesian language. Therefore, this research does an affective lexicon dataset called Indonesian Valence and Arousal Words (IVAW) containing 1024 words by Self-Assessment Manikin (SAM) surveys. Furthermore, for the next study, we will also crawls status in twitter based on selected words from IVAW to get Indonesian Valence and Arousal Text (IVAT). To predict VA rating for obtaining the advance of annotation quality, experiment will be compared by brain signal using EEG tool.
机译:文本分析领域的研究将始终与单词相关,无论是要使用的单词的选择还是句子中单词的位置。此外,一种假设是每种语言的差异会导致不同的含义,这使得一些研究人员有兴趣进行基于情感或情感单词的单词分类研究。研究集中于情感状态,作为对效价和唤醒维度的连续数值。情感分析通常是使用正面和负面类别方法进行的,如今,量纲方法可以提供更多对颗粒感的分析。另一方面,具有价数和唤醒等级的情感词数据集仍然非常稀少,尤其是对于印尼语而言。因此,这项研究通过“自我评估模型”(SAM)调查,创建了一个情感词典数据集,称为“印尼价和惯用语”(IVAW),其中包含1024个单词。此外,在下一个研究中,我们还将根据IVAW的选定单词在Twitter中抓取状态,以获取印尼价和唤醒文本(IVAT)。为了预测VA等级以获得注解质量的提高,将使用EEG工具通过脑信号比较实验。

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