Özet:

Open and Distance education is the form of education that delivers pedagogy, technology, and instructional designs to students who are not physically available at the same place in a traditional classroom or campus. Opinion mining (also called sentiment analysis) plays an important role in the field of social media. It computes people’s polarities such as positive, negative, neutral, which were expressed in online social media contents at various levels, namely, document level, sentence level, and corpus level. In this research paper, a multi-aspect based opinion mining system is proposed by applying opinion mining techniques for Open and Distance Education social media contents. The purpose of this research is to measure the public satisfaction of open and distance at the title level, document level, sentence level, and aspect level. The proposed system was employed by the data collection process, preprocessing, feature extraction, opinion detection and polarity classification using the Naïve Bayles classifier. The detected opinions at various levels are also visualized. The performance of the system is evaluated using precision, recall, f-measure, and accuracy.

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