Open AccessPerformance analysis of support vector machines with polynomial kernel for sentiment polarity identification: A case study in lecturer’s performance questionnaire
Author(s) -
Gede Aditra Pradnyana,
I Gede Mahendra Darmawiguna,
D K S Suditresna Jaya,
A Sasmita
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1810/1/012033
Subject(s) - support vector machine , polarity (international relations) , sentiment analysis , kernel (algebra) , computer science , identification (biology) , polynomial kernel , process (computing) , polynomial , sentence , artificial intelligence , machine learning , natural language processing , kernel method , mathematics , combinatorics , biology , mathematical analysis , genetics , botany , cell , operating system
The lecturers’ performance evaluation process can be carried out using an open questionnaire filled out by students at the end of the semester. In this questionnaire, students can provide an assessment in the form of comments, suggestions, and criticism of the ’lecturer’s performance, which can describe the level of student satisfaction with the lecture process. Conducting assessments or analyses on the open questionnaire entries manually will certainly impact the high costs time and energy. Sentiment polarity identification is a process in sentiment analysis that classifies text into a sentence or document and then determines whether the opinion expressed is positive, negative or neutral. In this research, a sentiment polarity detection system was developed in a lecturer evaluation questionnaire using the Support Vector Machine (SVM) method with a polynomial kernel. The test results showed that the SVM method’s performance with the Polynomial kernel was strongly influenced by the value of the learning rate parameter, the maximum iteration, and the degree, with the optimal parameter values, respectively, 0.001, 200, and 0.3. The use of optimal parameter values in the process of identifying sentiment polarity obtained an accuracy value of 84.88%.