Learning sentiment from students’ feedback for real-time interventions in classrooms

Knowledge about users sentiments can be used for a variety of adaptation purposes. In the case of teaching, knowledge about students sentiments can be used to address problems like confusion and boredom which affect students engagement. For this purpose, we looked at several methods that could be used for learning sentiment from students feedback. Thus, Naive Bayes, Complement Naive Bayes (CNB), Maximum Entropy and Support Vector Machine (SVM) were trained using real students' feedback. Two classifiers stand out as better at learning sentiment, with SVM resulting in the highest accuracy at 94%, followed by CNB at 84%. We also experimented with the use of the neutral class and the results indicated that, generally, classifiers perform better when the neutral class is excluded

Semi-Supervised Domain Adaptation and Collaborative Deep Learning for Dual Sentiment Analysis

Sentiment classification is a much needed topic that has grabbed the interest of many researchers. Especially, classification of data from customer reviews on various commercial products has been an important source of research. A model called supervised dual sentiment analysis is used to handle the polarity shift problem that occurs in sentiment classification. Labeling the reviews is a tedious and time consuming process. Even, a classifier trained on one domain may not perform well on the other domain. To overcome these limitations, in this paper we propose semi-supervised domain adaptive dual sentiment analysis that train a domain independent classifier with few labeled data. Reviews are of varying length and hence, classification is more accurate if long term dependency between the words is considered. We propose a collaborative deep learning approach to the dual sentiment analysis. Long short term memory (LSTM) recurrent neural network is used to handle sequence prediction to classify the reviews more accurately. LSTM takes more time to extract features from the reviews. Convolution neural network is used before LSTM layers to extract features resulting in the reduction of training time compared to LSTM alone

Transfer Learning using Computational Intelligence: A Survey

Abstract Transfer learning aims to provide a framework to utilize previously-acquired knowledge to solve new but similar problems much more quickly and effectively. In contrast to classical machine learning methods, transfer learning methods exploit the knowledge accumulated from data in auxiliary domains to facilitate predictive modeling consisting of different data patterns in the current domain. To improve the performance of existing transfer learning methods and handle the knowledge transfer process in real-world systems, ..