Bloom-epistemic and sentiment analysis hierarchical classification in
  course discussion forums
H. Toba Y. T. Hernita M. Ayub M. C. Wijanto
Online discussion forums are widely used for active textual interaction
between lecturers and students, and to see how the students have progressed in
a learning process. The objective of this study is to compare appropriate
machine-learning models to assess sentiments and Bloom\'s epistemic taxonomy
based on textual comments in educational discussion forums. Our proposed method
is called the hierarchical approach of Bloom-Epistemic and Sentiment Analysis
(BE-Sent). The research methodology consists of three main steps. The first
step is the data collection from the internal discussion forum and YouTube
comments of a Web Programming channel. The next step is text preprocessing to
annotate the text and clear unimportant words. Furthermore, with the text
dataset that has been successfully cleaned, sentiment analysis and epistemic
categorization will be done in each sentence of the text. Sentiment analysis is
divided into three categories: positive, negative, and neutral. Bloom\'s
epistemic is divided into six categories: remembering, understanding, applying,
analyzing, evaluating, and creating. This research has succeeded in producing a
course learning subsystem that assesses opinions based on text reviews of
discussion forums according to the category of sentiment and epistemic
analysis.