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Identifying emotions in social media: Comparison of word-emotion lexicons

KuĊĦen,Ema
; 
Cascavilla,Giuseppe
; 
Figl,Kathrin
; 
Conti,Mauro
; 
Strembeck,Mark
Abstract
In recent years, emotions expressed in social media messages have become a vivid research topic due to their influence on the spread of misinformation and online radicalization over online social networks. Thus, it is important to correctly identify emotions in order to make inferences from social media messages. In this paper, we report on the performance of three publicly available word-emotion lexicons (NRC, DepecheMood, EmoSenticNet) over a set of Facebook and Twitter messages. To this end, we designed and implemented an algorithm that applies natural language processing (NLP) techniques along with a number of heuristics that reflect the way humans naturally assess emotions in written texts. In order to evaluate the appropriateness of the obtained emotion scores, we conducted a questionnaire-based survey with human raters. Our results show that there are noticeable differences between the performance of the lexicons as well as with respect to emotion scores the human raters provided in our survey.
Description
Publisher Copyright: Âİ 2017 IEEE.
Date
2017-11-16
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Publisher
Institute of Electrical and Electronics Engineers Inc.
Collections
Tilburg University Research Output Collection
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Keywords
Emotions, Social network, Word-emotion lexicon
Citation
KuĊĦen, E, Cascavilla, G, Figl, K, Conti, M & Strembeck, M 2017, Identifying emotions in social media : Comparison of word-emotion lexicons. in I Awan, F Portela & M Younas (eds), Proceedings - 2017 5th International Conference on Future Internet of Things and Cloud Workshops, W-FiCloud 2017. Proceedings - 2017 5th International Conference on Future Internet of Things and Cloud Workshops, W-FiCloud 2017, vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 132-137, 5th IEEE International Conference on Future Internet of Things and Cloud Workshops, W-FiCloud 2017, Prague, Czech Republic, 21/08/17. https://doi.org/10.1109/FiCloudW.2017.75
URI
https://hdl.handle.net/20.500.14602/25978
License
info:eu-repo/semantics/openAccess
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