Authors: Florian Krebs ; Bruno Lubascher ; Tobias Moers ; Pieter Schaap and Gerasimos Spanakis

Affiliation: Maastricht University, Netherlands

ISBN: 978-989-758-275-2

ISSN: 2184-433X

Keyword(s): Emotion Mining, Social Media, Deep Learning, Natural Language Processing.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Knowledge Discovery and Information Retrieval ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Natural Language Processing ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems ; Theory and Methods

Abstract: As of February 2016 Facebook allows users to express their experienced emotions about a post by using five so-called ‘reactions’. This research paper proposes and evaluates alternative methods for predicting these reactions to user posts on public pages of firms/companies (like supermarket chains). For this purpose, we collected posts (and their reactions) from Facebook pages of large supermarket chains and constructed a dataset which is available for other researches. In order to predict the distribution of reactions of a new post, neural network architectures (convolutional and recurrent neural networks) were tested using pretrained word embeddings. Results of the neural networks were improved by introducing a bootstrapping approach for sentiment and emotion mining on the comments for each post. The final model (a combination of neural network and a baseline emotion miner) is able to predict the reaction distribution on Facebook posts with a mean squared error (or misclassification rate) of 0.135. (More)

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Paper citation in several formats:
Krebs, F.; Lubascher, B.; Moers, T.; Schaap, P. and Spanakis, G. (2018). Social Emotion Mining Techniques for Facebook Posts Reaction Prediction.In Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-275-2, ISSN 2184-433X, pages 211-220. DOI: 10.5220/0006656002110220

author={Florian Krebs. and Bruno Lubascher. and Tobias Moers. and Pieter Schaap. and Gerasimos Spanakis.},
title={Social Emotion Mining Techniques for Facebook Posts Reaction Prediction},
booktitle={Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},


JO - Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Social Emotion Mining Techniques for Facebook Posts Reaction Prediction
SN - 978-989-758-275-2
AU - Krebs, F.
AU - Lubascher, B.
AU - Moers, T.
AU - Schaap, P.
AU - Spanakis, G.
PY - 2018
SP - 211
EP - 220
DO - 10.5220/0006656002110220

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