社交媒体情境下的情感分析研究综述

被引：25

作者：

谭荧 ^{[1
]}

张进 ^{[2
]}

夏立新 ^{[1
]}

机构：

[1] 华中师范大学信息管理学院

[2] 威斯康星大学密尔沃基分校信息研究院

来源：

数据分析与知识发现 | 2020年 / 4卷 / 01期

关键词：

社交媒体; 情感分析; 情感分析任务;

D O I：

暂无

中图分类号：

G353.1 [情报资料的分析和研究]; TP391.1 [文字信息处理];

学科分类号：

1205 ; 081203 ; 0835 ;

摘要：

【目的】调研近年来社交媒体情境下的情感分析相关研究,重点介绍情感挖掘的任务和方法。【文献范围】利用Web of Science核心数据库检索2015年-2019年间,主题为Social Media和Sentiment Analysis的文献,并结合引文分析和浏览的方法补充文献集,共计收集163篇并引用代表性文献91篇。【方法】针对社交媒体情境下的情感分析研究方向、技术和应用进行内容分析。【结果】归纳10余种情感分析任务,总结适用于社交媒体平台的情感分析改进方法,并论述了这些情感分析结果的应用领域。【局限】未深入解析情感分析算法的步骤和过程。【结论】本文分析了情感分析研究的现有核心技术和改进方向,发现了该领域在社交媒体情境下的不同任务和挑战。

引用

页码：1 / 11

页数：11

共 39 条

[11]

Mining and summarizing customer reviews. HU M,LIU B. Proceedings of the 10th ACM SIGKDD International Conference onKnowledge Discovery And Data Mining . 2004

[12]

Sentiment Analysis of Product Reviews Using Deep Learning. Panthati J,Bhaskar J,Ranga T K, et al. Proceedings of the2018 International Conference on Advances in Computing,Communications and Informatics . 2018

[13]

Location Based Real-time Sentiment Analysis of Top Trending Event Using Hybrid Approach. Haripriya A,Kumari S,Babu C N. Proceedings of the 2018 International Conference on Advances in Computing, Communications and Informatics . 2018

[14]

Biasness Identification of Talk Show’’s Host by Using Twitter Data. Ijaz S,Lali M I,Shahzad B, et al. Proceedings of the 13th International Conference on Emerging Technologies . 2017

[15]

A shallow approach to subjectivity classification. Raaijmakers S,Kraaij W. 2008International Conference on Weblogs and Social Media（ICWSM） . 2008

[16]

Semi-supervised recursive autoencoders for predicting sentiment distributions. Socher R,Pennington J,Huang E H,et al. Proceedings of the Conference on Empirical Methods in Natural Language Processing . 2011

[17]

Sentiment-Based Model for Recommender Systems. Osman N A,Noah S A M. Proceedings of the 4thInternational Conference on Information Retrieval and Knowledge Management . 2018

[18]

An Approach of Fuzzy Relation Equation and Fuzzy-rough Set for Multi-label Emotion Intensity Analysis. Wang C,Wang D,Feng S, et al. Proceedings of the 2016 International Conference on Database Systems for Advanced Applications . 2016

[19]

A survey of multimodal sentiment analysis[J] . Mohammad Soleymani,David Garcia,Brendan Jou,Bj?rn Schuller,Shih-Fu Chang,Maja Pantic.Image and Vision Computing . 2017

[20]

Prediction from regional angst – A study of NFL sentiment in Twitter using technical stock market charting[J] . Robert P. Schumaker,Chester S. Labedz,A. Tomasz Jarmoszko,Leonard L. Brown.Decision Support Systems . 2017

← 1 2 3 4 →