TY - JOUR
T1 - A survey on sentiment analysis and opinion mining for social multimedia
AU - Li, Zuhe
AU - Fan, Yangyu
AU - Jiang, Bin
AU - Lei, Tao
AU - Liu, Weihua
N1 - Publisher Copyright:
© 2018, Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2019/3/1
Y1 - 2019/3/1
N2 - Social media sentiment analysis (also known as opinion mining) which aims to extract people’s opinions, attitudes and emotions from social networks has become a research hotspot. Conventional sentiment analysis concentrates primarily on the textual content. However, multimedia sentiment analysis has begun to receive attention since visual content such as images and videos is becoming a new medium for self-expression in social networks. In order to provide a reference for the researchers in this active area, we give an overview of this topic and describe the algorithms of sentiment analysis and opinion mining for social multimedia. Having conducted a brief review on textual sentiment analysis for social media, we present a comprehensive survey of visual sentiment analysis on the basis of a thorough investigation of the existing literature. We further give a summary of existing studies on multimodal sentiment analysis which combines multiple media channels. We finally summarize the existing benchmark datasets in this area, and discuss the future research trends and potential directions for multimedia sentiment analysis. This survey covers 100 articles during 2008–2018 and categorizes existing studies according to the approaches they adopt.
AB - Social media sentiment analysis (also known as opinion mining) which aims to extract people’s opinions, attitudes and emotions from social networks has become a research hotspot. Conventional sentiment analysis concentrates primarily on the textual content. However, multimedia sentiment analysis has begun to receive attention since visual content such as images and videos is becoming a new medium for self-expression in social networks. In order to provide a reference for the researchers in this active area, we give an overview of this topic and describe the algorithms of sentiment analysis and opinion mining for social multimedia. Having conducted a brief review on textual sentiment analysis for social media, we present a comprehensive survey of visual sentiment analysis on the basis of a thorough investigation of the existing literature. We further give a summary of existing studies on multimodal sentiment analysis which combines multiple media channels. We finally summarize the existing benchmark datasets in this area, and discuss the future research trends and potential directions for multimedia sentiment analysis. This survey covers 100 articles during 2008–2018 and categorizes existing studies according to the approaches they adopt.
KW - Multimedia sentiment
KW - Opinion mining
KW - Sentiment analysis
KW - Social media
UR - http://www.scopus.com/inward/record.url?scp=85051683643&partnerID=8YFLogxK
U2 - 10.1007/s11042-018-6445-z
DO - 10.1007/s11042-018-6445-z
M3 - 文章
AN - SCOPUS:85051683643
SN - 1380-7501
VL - 78
SP - 6939
EP - 6967
JO - Multimedia Tools and Applications
JF - Multimedia Tools and Applications
IS - 6
ER -