Implementing Support Vector Machine Sentiment Analysis to Students' Opinion toward Lecturer in an Indonesian Public University

Abstract

Student feedback is an important evaluation tool for quality improvement. Moreover, in Indonesia's higher education system there is an assessment regulation that puts special attention to the availability of the student feedback system. However, parts of the questionnaire are in the form of descriptive text that requires more effort for analysis. This situation leads to a very tiresome work in case of the number of documents reaches several hundred or even thousands. There were some efforts to apply computer-assisted classification by utilizing machine learning, however, most of them only analyzed English documents. Only a handful that studied the classification of documents in Bahasa Indonesia. In reality, we found some cases where the students used mixed languages while filling the evaluation forms. Therefore, in this study, we expand the application of text classification by using Support Vector Machne (SVM) to cases of student feedback in mixed languages. The model was built computationally and from the test, we get 74% accuracy and 0.46 Kappa value.

Effects of kernels and the proportion of training data on the accuracy of SVM sentiment analysis in lecturer evaluation

“…The data for this study is acquired from the lecturer evaluation/student feedbacks of the spring semester 2019. The same data was used in [14]. In May to mid-June, students were asked to fill an anonymous online evaluation form for each lecturer they had during the semester that was about to end.…”

“…Although stemming is commonly used in the pre-processing phase, in this study this step was omitted, as was done in [14]. The reason behind this is that stemming has been identified to bring no significant impact to the accuracy when applied to documents with Bahasa Indonesia [26].…”

“…Educational sentiment analysis is a field of SVM application. This field commonly addresses the students' evaluations of the lecturer [14][15][16][17][18][19]. In one of our former work [14], we have applied support vector machine (SVM) to classify the sentiments in lecturer evaluation by students.…”

“…This field commonly addresses the students' evaluations of the lecturer [14][15][16][17][18][19]. In one of our former work [14], we have applied support vector machine (SVM) to classify the sentiments in lecturer evaluation by students. In [15], Altrabsheh et al proposed the sentiment analysis for education (SA-E), which is a system that will collect students' feedback by using the social network and then processed them by using SVM and/or naive bayes.…”

Palaute: An Online Text Mining Tool for Analyzing Written Student Course Feedback

“…The current study focuses on the added value provided by open-ended, written student feedback. The automatic analysis of open-ended feedback using text mining and machine learning tools is a recent trend in higher education research (see for example [13]- [30]). Extant literature has…”