文献类型：期刊文献

中文题名：基于词典和弱标注信息的电影评论情感分析

英文题名：Sentiment analysis of movie reviews based on dictionary and weak tagging information

作者：樊振[1];过弋[1,2];张振豪[1];韩美琪[1]

机构：[1]华东理工大学信息科学与工程学院,上海200237;[2]石河子大学信息科学与技术学院,新疆石河子832003

年份：2018

卷号：38

期号：11

起止页码：3084

中文期刊名：计算机应用

外文期刊名：journal of Computer Applications

收录：CSTPCD;;北大核心:【北大核心2017】；CSCD:【CSCD_E2017_2018】；

基金：国家自然科学基金资助项目(61462073);上海市科学技术委员会科研计划项目(17DZ1101003;18511106602)~~

语种：中文

中文关键词：电影评论;情感词典;弱标注信息;支持向量机;情感分类

外文关键词：movie review;sentiment dictionary;weak tagging information;Support Vector Machine(SVM);sentiment classification

摘要：针对评论文本情感分析研究中数据标注费时费力的问题,提出了一种新的数据自动标注方法。首先,通过基于情感词典的方法计算出评论文本的情感倾向;其次,利用用户评分的弱标注信息和基于词典方法的情感倾向对评论文本自动标注;最后,利用支持向量机(SVM)对评论文本进行情感分类。所提出的数据自动标注方法在两种类型数据集情感分类准确率上分别达到了77.2%和77.8%,相对于单一的利用用户评分对数据标注的方法,分别提高了1.7个百分点和2.1个百分点。实验结果表明,提出的数据自动标注方法在电影评论情感分析中能提高分类效果。
Focused on the time-consuming and laborious problem of data annotation in review text sentiment analysis,a new automatic data annotation method was proposed.Firstly,the sentiment tendency of the review text was calculated based on the sentiment dictionary.Secondly,the review text was automatically annotated by using the weak tagging information of the user and the sentiment tendency based on the dictionary.Finally,Support Vector Machine(SVM)was used to classify the sentiment of the review text.The proposed method reached 77.2%and 77.8%respectively in the accuracy of sentiment classification on two types of data sets,which were 1.7 percentage points and 2.1 percentage points respectively higher than those of the method only based on user rating.The experimental results show that the proposed method can improve the classification effect in movie reviews sentiment analysis.