Aspect-Based Extraction and Analysis of Affective Knowledge from Social Media Streams

Extracting and analyzing affective knowledge from social media in a structured manner is a challenging task. Decision makers require insights into the public perception of a company's products and services, as a strategic feedback channel to guide communication campaigns, and as an early warnin...

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Bibliographic Details
Published in:IEEE intelligent systems 2017-05, Vol.32 (3), p.80-88
Main Authors: Weichselbraun, Albert, Gindl, Stefan, Fischer, Fabian, Vakulenko, Svitlana, Scharl, Arno
Format: Article
Language:English
Subjects:
affective knowledge extraction
Analytics
artificial intelligence
aspect-based sentiment analysis
Automobiles
Categories
Communications systems
Companies
Data mining
Digital media
Early warning systems
Emotions
Extraction
Feedback
Knowledge
Knowledge acquisition
Knowledge base
linked data
Mathematical analysis
Media
opinion targets
Perception
Product design
Product specifications
Sentiment analysis
social media
Social network services
Social networks
Target recognition
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Summary:Extracting and analyzing affective knowledge from social media in a structured manner is a challenging task. Decision makers require insights into the public perception of a company's products and services, as a strategic feedback channel to guide communication campaigns, and as an early warning system to quickly react in the case of unforeseen events. The approach presented in this article goes beyond bipolar metrics of sentiment. It combines factual and affective knowledge extracted from rich public knowledge bases to analyze emotions expressed toward specific entities (targets) in social media. The authors obtain common and common-sense domain knowledge from DBpedia and ConceptNet to identify potential sentiment targets. They employ affective knowledge about emotional categories available from SenticNet to assess how those targets and their aspects (such as specific product features) are perceived in social media. An evaluation shows the usefulness and correctness of the extracted domain knowledge, which is used in a proof-of-concept data analytics application to investigate the perception of car brands on social media in the period between September and November 2015.
ISSN:1541-1672
1941-1294
DOI:10.1109/MIS.2017.57