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Microblog sentiment analysis using social and topic context
- Source :
- PLoS ONE, Vol 13, Iss 2, p e0191163 (2018), PLoS ONE
- Publication Year :
- 2018
- Publisher :
- Public Library of Science (PLoS), 2018.
-
Abstract
- Analyzing massive user-generated microblogs is very crucial in many fields, attracting many researchers to study. However, it is very challenging to process such noisy and short microblogs. Most prior works only use texts to identify sentiment polarity and assume that microblogs are independent and identically distributed, which ignore microblogs are networked data. Therefore, their performance is not usually satisfactory. Inspired by two sociological theories (sentimental consistency and emotional contagion), in this paper, we propose a new method combining social context and topic context to analyze microblog sentiment. In particular, different from previous work using direct user relations, we introduce structure similarity context into social contexts and propose a method to measure structure similarity. In addition, we also introduce topic context to model the semantic relations between microblogs. Social context and topic context are combined by the Laplacian matrix of the graph built by these contexts and Laplacian regularization are added into the microblog sentiment analysis model. Experimental results on two real Twitter datasets demonstrate that our proposed model can outperform baseline methods consistently and significantly.
- Subjects :
- Computer and Information Sciences
Support Vector Machine
Blogging
Microblogging
Computer science
Emotions
Twitter
InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL
Social Sciences
lcsh:Medicine
Emotional contagion
02 engineering and technology
Social Theory
Machine Learning
Text mining
Sociology
Artificial Intelligence
020204 information systems
Support Vector Machines
0202 electrical engineering, electronic engineering, information engineering
Humans
Psychology
Social media
Least-Squares Analysis
lcsh:Science
Natural Language Processing
Lexicons
Multidisciplinary
Information retrieval
Models, Statistical
business.industry
Sentiment analysis
lcsh:R
Social Communication
Biology and Life Sciences
Linguistics
Sociological Factors
Communications
Semantics
Support vector machine
Social Networks
Graph (abstract data type)
020201 artificial intelligence & image processing
lcsh:Q
business
Social Media
Network Analysis
Social theory
Research Article
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 13
- Issue :
- 2
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....47ff7fcae997b61dc8c28b88bf479cca