Sentiment analysis in Facebook and its application to e-learning

• Published in: Computers in human behavior Vol. 31; pp. 527 - 541
• Main Authors: Ortigosa, Alvaro, Martín, José M., Carro, Rosa M.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-02-2014
• Subjects: Adaptive e-learning; Adaptive systems; Classification; Data mining; Distance education; Distance learning; Messages; Polarity; Sentiment analysis; Social networks; Teachers; User modeling; Adaptive e-learning; Sentiment analysis; Social networks; User modeling
• ISSN: 0747-5632; 1873-7692
• DOI: 10.1016/j.chb.2013.05.024

•We describe a new method to support sentiment analysis in Facebook.•We have implemented it in SentBuk, a Facebook application.•We report results when using lexicon-based, machine-learning and hybrid approaches.•The best accuracy was reached through the hybrid approach (83.27%).•We propose several applications of this approach for e-learning.
This paper presents a new method for sentiment analysis in Facebook that, starting from messages written by users, supports: (i) to extract information about the users’ sentiment polarity (positive, neutral or negative), as transmitted in the messages they write; and (ii) to model the users’ usual sentiment polarity and to detect significant emotional changes. We have implemented this method in SentBuk, a Facebook application also presented in this paper. SentBuk retrieves messages written by users in Facebook and classifies them according to their polarity, showing the results to the users through an interactive interface. It also supports emotional change detection, friend’s emotion finding, user classification according to their messages, and statistics, among others. The classification method implemented in SentBuk follows a hybrid approach: it combines lexical-based and machine-learning techniques. The results obtained through this approach show that it is feasible to perform sentiment analysis in Facebook with high accuracy (83.27%). In the context of e-learning, it is very useful to have information about the users’ sentiments available. On one hand, this information can be used by adaptive e-learning systems to support personalized learning, by considering the user’s emotional state when recommending him/her the most suitable activities to be tackled at each time. On the other hand, the students’ sentiments towards a course can serve as feedback for teachers, especially in the case of online learning, where face-to-face contact is less frequent. The usefulness of this work in the context of e-learning, both for teachers and for adaptive systems, is described too.