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中文摘要：
词语级的情感倾向性分析一直是文本情感计算领域的热点研究方向，如何自动识别情感新词，并判断其情感倾向性已经成为当前亟待解决的问题。首先用基于统计量的方法识别微博语料中的新词，然后利用神经网络去训练语料中词语的词向量，从语料自身挖掘出词与词之间的相关性，最后提出了基于词向量的情感新词发现方法。实验表明该方法可以有效应用于情感新词发现。
英文摘要：
Word-level sentiment analysis is a hot research interest in the field of affective computing.How to recognize and analyze these new emotional words automatically becomes an urgent problem.Firstly,statistics-based approach was used to identify the new words in Micro-blog corpus and then distributed representation of new words was trained by u-sing neural network in order to get the correlation between words in corpus.Finally three vector-based methods to find new emotional words were introduced.The experimental results indicate that the proposed methods in this paper can be effectively used in discovery of new emotional words.
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