Sentiment Analysis of Teachers Using Social Information in Educational Platform Environments

Abstract

Learners’ opinions constitute an important source of information that can be useful to teachers and educational instructors in order to improve learning procedures and training activities. By analyzing learners’ actions and extracting data related to their learning behavior, educators can specify proper learning approaches to stimulate learners’ interest and contribute to constructive monitoring of learning progress during the course or to improve future courses. Learners-generated content and their feedback and comments can provide indicative information about the educational procedures that they attended and the training activities that they participated in. Educational systems must possess mechanisms to analyze learners’ comments and automatically specify their opinions and attitude towards the courses and the learning activities that are offered to them. This paper describes a Greek language sentiment analysis system that analyzes texts written in Greek language and generates feature vectors which together with classification algorithms give us the opportunity to classify Greek texts based on the personal opinion and the degree of satisfaction expressed. The sentiment analysis module has been integrated into the hybrid educational systems of the Greek school network that offers life-long learning courses. The module offers a wide range of possibilities to lecturers, policymakers and educational institutes that participate in the training procedure and offers life-long learning courses, to understand how their learners perceive learning activities and specify what aspects of the learning activities they liked and disliked. The experimental study show quite interesting results regarding the performance of the sentiment analysis methodology and the specification of users’ opinions and satisfaction. The feature analysis demonstrates interesting findings regarding the characteristics that provide indicative information for opinion analysis and embeddings combined with deep learning approaches yield satisfactory results.

A Review of Sentiment Analysis Approaches for Quality Assurance in Teaching and Learning

“…Supervised and lexicon-based [52], [53], [54], [55] Table 7 show emphasis on supervised learning models that are wildly studied for sentiment analysis in education domain. These models include the Decision Tree (DT), Support Vector Machine (SVM), K Nearest Neighbor (KNN), Naïve Bayes (NB) and Neural Network (NN).…”

FBO‐RNN: Fuzzy butterfly optimization‐based RNN‐LSTM for extracting sentiments from Twitter Emoji database

“…For sentiment classification, the freeware WEKA software tool performs experimental investigations. 55 A variety of evaluations are used to evaluate the effectiveness of the proposed approach, and it is compared against cutting-edge methodologies. The parameter configuration with respect to the LSTM-based FBO algorithm for SA is depicted in Table 3.…”

A Review of Sentiment Analysis Approaches for Quality Assurance in Teaching and Learning (RETRACTED)

“…Supervised and unsupervised [48], [49], [50], [51]. Supervised and lexicon-based [52], [53], [54], [55], [56], [57], [58], [59], [60], [61]. Unsupervised and lexicon-based [62], [63], [64].…”