Social Emotion Mining Techniques for Facebook Posts Reaction Prediction

Florian Krebs, Bruno Lubascher, Tobias Moers, Pieter Schaap, Gerasimos Spanakis

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.

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Paper Citation


in Harvard Style

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 2: ICAART, ISBN 978-989-758-275-2, pages 211-220. DOI: 10.5220/0006656002110220


in Bibtex Style

@conference{icaart18,
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 2: ICAART,},
year={2018},
pages={211-220},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006656002110220},
isbn={978-989-758-275-2},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 2: 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