基于ER Rule的多分类器汽车评论情感分类研究  

Multi-classifier for Car Review Sentiment Classification Based on ER Rule

作　　者：周谧[1,2] 周雅婧 贺洋 方必和 ZHOU Mi;ZHOU Yajing;HE Yang;FANG Bihe(School of Management,Hefei University of Technology,Hefei 230009,China;Engineering Research Center for Intelligent Decision-making and Information System Technologies,Ministry of Education,Hefei 230009,China)

机构地区：[1]合肥工业大学管理学院,安徽合肥230009 [2]智能决策与信息系统技术教育部工程研究中心,安徽合肥230009

出　　处：《运筹与管理》2024年第5期161-168,共8页Operations Research and Management Science

基　　金：国家自然科学基金资助项目(71521001);NSFC-浙江两化融合项目(U1709215)。

摘　　要：该文针对汽车评论语料的情感二分类问题,提出一种基于证据推理规则的多分类器融合的情感分类方法。在情感特征构建方面,通过实验对比不同特征模型对分类结果的影响,并改进传统的TFIDF权重计算方法。同时,在此基础上使用ER Rule融合不同分类器进行文本情感极性分析,并考虑各分类器的权重和可靠度。最后,爬取汽车网站上的评论数据对上述方法进行测试,并用公开的中文酒店评论语料数据进行了验证,结果表明该方法能够有效集成不同分类器的优点,与传统机器学习分类算法相比,其结果在Recall,F1值和Accuracy三个指标上得到了提高,与目前流行的深度学习算法和集成学习算法相比,其结果总体占优。With the rapid development of the next-generation information technology,more and more users are accustomed to sharing personal experience and opinions through the Internet,such as online reviews of book,movie,product usage experience and so on,which always contain positive and negative sentiment of users.Text sentiment analysis aims to use computer technology to detect and extract diverse sentiments,attitudes,opinions and other perceptual information in text documents,thereby converting qualitative user expressions into quantifiable data to serve decision-making and strategic planning.For users,these product reviews can provide them with sufficient information that will help them make informed purchasing decisions to the greatest extent and minimize the degree of regret after consumption.For manufacturers,consumers’needs can be acquired timely through the reviews,thus adjusting their marketing strategies in a targeted manner and improving the design and quality of products.Currently,due to the exponential growth in the number of these review texts on the Internet,traditional manual analysis methods can hardly satisfy the rapidly changing market demand.Deep learning-based methods may fall into the dilemma of weak interpretability.Therefore,how to automatically obtain users’sentiment information from numerous comments via a rational and intelligent way is a challenging issue.For the problem of sentimental dichotomy on car commentary corpus,a text sentiment classification method based on ER rule multi-classifier fusion is proposed in this paper.Firstly,the research explores sentiment feature construction by examining the classification effects of various feature models,including unigram,bigram and unigram+bigram.The CHI Square test is adopted for text feature extraction.This method is particularly effective in managing high-dimensional feature spaces,facilitating more accurate sentiment classification by highlighting the most relevant features for analysis.Secondly,the improved TF-IDF method is proposed to enha

关 键 词：证据推理规则 多分类器融合 TFIDF权重 深度学习算法 集成学习算法 

分 类 号：TP391.43[自动化与计算机技术—计算机应用技术]