基于句子级情感的中文网络评论的情感极性分类

Classification of sentimental polarity for Chinese online reviews based on sentence level sentiment

为提高网络评论段落的情感极性分类准确率,在考虑人们表达习惯和语料粒度的基础上,提出一种基于句子情感的段落情感极性分类方法.该方法通过句子的情感极性和句子的情感极性贡献度来对段落进行情感分类,采用传统分类方法预测句子的情感极性,提出等权重、相关度、情感条件假设3种方法,能够根据训练语料的统计数据动态地确定段落中不同位置句子的情感极性贡献度.最后,以超过2个句子的手机和酒店网络评论为对象进行实验分析,实验结果显示,与传统方法相比,考虑了人们表达习惯的相关度和情感条件假设方法显著提高了段落分类的准确率,且具有一定的自适应性.

With the boost of online reviews, sentiment polarity classification rises in response to the requirement of retrieving consumers＇ positive or negative opinions on certain products. The primary goal of this research is to improve the accuracy of sentiment polarity classification at the level of paragraphs for Chinese online reviews. With a view to the ways of expression and the grain of corpus, this paper presents a method to predict the sentiment polarity of Chinese online reviews in paragraphs based on sentence level sentiment analysis. Firstly, traditional classification methods are applied to predict the sentiment polarity of sentence. Then, three different algorithms i. e. , the equal weight, correlation degree and assumption of sentiment condition, are employed to calculate the contribution that each sentence lying in the different positions of paragraph makes to the sentiment polarity of paragraph. Finally an experiment has been made based on hotel and mobile phone online reviews with lengths beyond two sentences. The result shows that the accuracy of sentiment polarity classification at the level of paragraph is remarkably increased by the method proposed in this paper, by taking correlation degree of expression and assumption of sentiment condition into consideration.