基于词向量的情感新词发现方法

New methods for extracting emotional words based on distributed representations of words

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。

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.