Social Emotion Mining Techniques for Facebook Posts Reaction Prediction

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Krebs, Florian ; Lubascher, Bruno ; Moers, Tobias ; Schaap, Pieter ; Spanakis, Gerasimos (2017)
  • Subject: Computer Science - Computation and Language | Computer Science - Artificial Intelligence | Computer Science - Information Retrieval

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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