基于粗糙集和多通道词向量的中文文本情感特征分析

粗糙集是一种能够有效处理不精确、不完备和不确定信息的数学工具,粗糙集的属性约简可以在保持文本情感分类能力不变的情况下对文本情感词特征进行约简。针对情感词特征空间维数过高、情感词特征表示缺少语义信息的问题,该文提出了RS-WvGv中文文本情感词特征表示方法。利用粗糙集决策表对整个语料库进行情感词特征建模,采用Johnson粗糙集属性约简算法对决策表进行化简,保留最小的文本情感词特征属性集,之后再对该集合中的所有情感特征词进行词嵌入表示,最后用逻辑回归分类器验证RS-WvGv方法的有效性。另外,该文还定义了情感词特征属性集覆盖力,用于表示文本情感词特征属性集合对语料库的覆盖能力。最后,在实验对比的过程中,用统计检验进一步验证了该方法的有效性。

Chinese Text Sentiment Feature Analysis Based on Rough Set and Multi Channel Word Vector

Rough set is a mathematical tool that can greatly reduce the dimension and number of text sentiment word features while keeping the ability of text sentiment classification unchanged. Aiming at the problem that the text sentiment word feature dimension is too high and the sentiment word feature representation lacks semantic information, this article proposes a novel Chinese text sentiment word feature representation method named RS-WvGv. The decision table of rough set is used to model the text sentiment word feature of the whole corpus. The Johnson attribute reduction algorithm is applied to simplify the decision table and get the minimum set of text sentiment word feature attributes. And then based on the word embedding of all the sentiment feature words in the set, the RS-WvGv method is verified with logistic regression classifier in the experiment.