Bloom-epistemic and sentiment analysis hierarchical classification in course discussion forums
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.
Code (0)
등록된 구현이 없습니다.
Tasks
SentenceSentiment AnalysisSimilar Papers 제목 키워드 기반
A Self-Attentive Hierarchical Model for Jointly Improving Text Summarization and Sentiment Classification
Text summarization and sentiment classification, in NLP, are two main tasks implemented on text analysis, focusing on extracting the major idea of a text at different levels. Based on the characteristics of both, senti…
Abstractive Text SummarizationClassificationGeneral ClassificationSentiment Analysis+2Optimizing Performance: How Compact Models Match or Exceed GPT's Classification Capabilities through Fine-Tuning
In this paper, we demonstrate that non-generative, small-sized models such as FinBERT and FinDRoBERTa, when fine-tuned, can outperform GPT-3.5 and GPT-4 models in zero-shot learning settings in sentiment analysis for fin…
Sentiment Analysistext-classificationText ClassificationZero-Shot LearningAspect-level Sentiment Classification with HEAT (HiErarchical ATtention) Network
Aspect-level sentiment classification is a fine-grained sentiment analysis task, which aims to predict the sentiment of a text in different aspects. One key point of this task is to allocate the appropriate sentiment wor…
ClassificationSentenceSentiment AnalysisSentiment ClassificationBloombergGPT: A Large Language Model for Finance
The use of NLP in the realm of financial technology is broad and complex, with applications ranging from sentiment analysis and named entity recognition to question answering. Large Language Models (LLMs) have been shown…
Causal JudgmentCommon Sense ReasoningDate UnderstandingDisambiguation QA+25Stress index strategy enhanced with financial news sentiment analysis for the equity markets
This paper introduces a new risk-on risk-off strategy for the stock market, which combines a financial stress indicator with a sentiment analysis done by ChatGPT reading and interpreting Bloomberg daily market summaries.…
Sentiment Analysis