CAJ | 학술논문

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。
사어급적정감경향성분석일직시문본정감계산영역적열점연구방향，여하자동식별정감신사，병판단기정감경향성이경성위당전극대해결적문제。수선용기우통계량적방법식별미박어료중적신사，연후이용신경망락거훈련어료중사어적사향량，종어료자신알굴출사여사지간적상관성，최후제출료기우사향량적정감신사발현방법。실험표명해방법가이유효응용우정감신사발현。
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.