Social Emotion Mining Techniques for Facebook Posts Reaction Prediction

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.

Measuring the Diversity of Facebook Reactions to Research

“…They found that generally the emotional content of emojis and reactions correspond, but that in instances of sarcasm or politeness, the two channels could express different meanings. Krebs et al [29] used customer satisfaction data gathered from Facebook to train a model using convolutional and recurrent neural networks (CNN and RNN) and predict the reaction distribution for a given post. Basile et al [30] combine NLP and sentiment analysis of Facebook reactions to build a regression model that predicts news controversies in Italian media.…”

Semantic Web Mining in Retail Management System using ANN

“…The outcomes of proposed technique have greater precision compared with the existing techniques and also it could be applied for the estimation of micro scale market potential. Krebs et al (2017), proposed a reaction prediction on the Facebook post by using neural networks. For that, a data set was used to find the Facebook post reaction, and it was useful for both marketing users and machine learners.…”

Shared Feelings: Understanding Facebook Reactions to Scholarly Articles

“…ACM/IEEE, June 2019, Urbana-Champaign, Illinois USA © 2019 Association for Computing Machinery. the knowledge online platforms provide about human behavior-an area of research known as social-media analytics [3,6,9]. Studies in social-media analytics tend to focus either on text, using approaches such as Natural Language Processing (NLP), sentiment analysis, or opinion mining to arrive at and support research conclusions [12], or on the proliferation of content through online communities [5].…”