Sentiment analysis in Facebook and its application to 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. The use of computers in education has meant a great contribution for students and teachers. The incorporation of adaptation methods and techniques allows the development of adaptive e-learning systems, where each student receives personalized guidance during the learning process (Brusilovsky, 2001). In order to provide personalization, it is necessary to store information about each student in what is called the student model (Kobsa, 2007). The specific information to be collected and stored depends on the goals of the adaptive e-learning system (e.g., preferences, learning styles, personality, emotional state, context, previous actions , and so on). In particular, affective and emotional factors, among other aspects, seem to affect the student motivation and, in general, the outcome of the learning process (Shen, Wang, & Shen, 2012). Therefore, in learning contexts, being able to detect and manage information about the students' emotions at a certain time can contribute to know their potential …