Social Emotion Mining Techniques for Facebook Posts Reaction Prediction

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.Comment: 10 pages, 13 figures and accepted at ICAART 2018. (Dataset: https://github.com/jerryspan/FacebookR

SEMTec: Social Emotion Mining Techniques for Analysis and Prediction of Facebook Post Reactions

Nowadays social media are utilized by many people in order to review products and services. Subsequently, companies can use this feedback in order to improve customer experience. Facebook provided its users with the ability to express their experienced emotions by using five so-called 'reactions'. Since this launch happened in 2016, this paper is one of the first approaches to provide a complete framework for evaluating different techniques for predicting reactions to user posts on public pages. For this purpose, we used the FacebookR dataset that contains Facebook posts (along with their comments and reactions) of the biggest international supermarket chains. In order to build a robust and accurate prediction pipeline state-of-the-art neural network architectures (convolutional and recurrent neural networks) were tested using pretrained word embeddings. The models are further improved by introducing a bootstrapping approach for sentiment and emotion mining on the comments for each post and a data augmentation technique to obtain an even more robust predictor. The final proposed pipeline is a combination of a neural network and a baseline emotion miner and is able to predict the reaction distribution on Facebook posts with a mean squared error (or misclassification rate) of 0.1326

inD Dataset Python Tools

The goal of this repo is to make the inD dataset as easy to use as possible. For this purpose we provide source code in Python, which allows the import and visualization of the dataset