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Construction of a Multi-dimensional Vectorized Affective Lexicon

机译：多维矢量化情感词典的构建

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Affective analysis has received growing attention from both research community and industry. However, previous works either cannot express the complex and compound states of human's feelings or rely heavily on manual intervention. In this paper, by adopting Plutchik's wheel of emotions, we propose a lowcost construction method that utilizes word embeddings and high-quality small seed-sets of affective words to generate multi-dimensional affective vector automatically. And a large-scale affective lexicon is constructed as a verification, which could map each word to a vector in the affective space. Meanwhile, the construction procedure uses little supervision or manual intervention, and could learn affective knowledge from huge amount of raw corpus automatically. Experimental results on affective classification task and contextual polarity disambiguation task demonstrate that the proposed affective lexicon outperforms other state-of-the-art affective lexicons.
机译：情感分析越来越受到研究界和行业的关注。但是，先前的作品无法表达人类情感的复杂和复合状态，或者严重依赖人工干预。本文采用普鲁奇克的情感轮，提出了一种低成本的构建方法，该方法利用词嵌入和高质量的情感词小种子集自动生成多维情感向量。并构建了一个大规模的情感词典作为验证，可以将每个单词映射到情感空间中的向量。同时，该构建过程几乎不需要监督或人工干预，并且可以从大量的原始语料库中自动学习情感知识。关于情感分类任务和上下文极性消歧任务的实验结果表明，所提出的情感词典优于其他最新的情感词典。

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