摘要
[目的/意义]在社会网络背景下,在线评论情感倾向已经成为影响观影者决策的重要因素,如何有效提高在线评论情感分析的准确性成为学者们关注的热点。[方法/过程]鉴于此,依据情感分析和概率语言术语集相关理论知识,提出一种新的影视推荐算法。首先运用TF-IDF算法提取主题词,确定主题词权重,然后计算在线评论情感值并细分区间确定情感程度,根据在线评论的情感程度构建概率语言决策矩阵,最后提出基于VIKOR的概率语言多准则决策方法,并将其用于电影排序。[结果/结论]采集Rotten Tomatoes官方网站上5部电影的真实在线评论数据,将文章提出的推荐算法与其他基于情感分析的推荐算法进行比较,验证所提出算法的可行性和优越性。
[Purpose/significance]Under the background of social network,sentiment analysis of online reviews has become an important factor affecting the decision-making of movie viewers.How to effectively improve the efficiency and accuracy of sentiment analysis of online reviews has become a hot topic for scholars.[Method/process]In view of this,a new movie recommendation algorithm is proposed based on sentiment analysis and probabilistic linguistic term sets.Firstly,the Term Frequency-Inverse Document Frequency(TF-IDF)algorithm is used to extract the keywords and determine the weight of the keywords.Then the emotional value of online comments is calculated and the emotional degree is determined by dividing the intervals.the probabilistic linguistic decision matrix is constructed according to the emotional degree of online comments.Finally,a VIKOR method based on probabilistic linguistic term sets is proposed.[Result/conclusion]The real online comment data of five movies on Rotten Tomatoes official website are collected and compared with other recommendation algorithms based on sentiment analysis to verify the feasibility and superiority of the proposed algorithm.
出处
《情报理论与实践》
CSSCI
北大核心
2020年第6期180-186,共7页
Information Studies:Theory & Application
基金
国家自然科学基金青年项目“社会网络下基于扩展灰数和云模型的影视推荐方法研究”(项目编号:71801090)
湖南省自然科学基金青年项目“媒介融合背景下基于扩展灰数和云模型的影视推荐方法研究”(项目编号:2018JJ3132)
湖南省社会科学成果评审委员会课题“基于犹豫模糊语言的湖南工业旅游资源评价”(项目编号:XSP18YBZ158)的研究成果。
关键词
在线评论
情感分析
概率语言术语集
VIKOR
影视推荐
online review
sentiment analysis
probabilistic linguistic term sets
VIKOR
movie recommendation
作者简介
通讯作者:周欢,女,1982年生,博士,副教授,硕士生导师。研究方向:决策理论与应用研究,数据挖掘;马浩南,女,1997年生,硕士生。研究方向:决策理论与应用研究,数据挖掘;刘嘉,男,1998年生,本科生。研究方向:机器学习。
