Projects per year
Abstract
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.
Original language | English |
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Pages (from-to) | 80 - 88 |
Journal | IEEE Intelligent Systems |
Volume | 32 |
Issue number | 3 |
DOIs | |
Publication status | Published - 2017 |
Austrian Classification of Fields of Science and Technology (ÖFOS)
- 102001 Artificial intelligence
Projects
- 1 Finished
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uComp (Embedded Human Computation for Knowledge Extraction and Evaluation)
Hornik, K., Wohlgenannt, G., Belk, S., Föls, M., Hanika, F., Karacsonyi, M., Konrad, P., Lang, H., Rohrer, K. & Schett, M.
1/11/12 → 31/10/15
Project: Research funding