Social Emotion Mining Techniques for Facebook Posts Reaction Prediction
This work addresses the problem of understanding user engagement for companies on social media, but it is incremental as it builds on existing emotion mining and neural network techniques.
The paper tackled predicting Facebook reaction distributions on company posts by testing neural networks with pretrained embeddings and improving them with a bootstrapping approach for sentiment and emotion mining on comments, achieving a mean squared error of 0.135.
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.