Sentiment analysis in learning resources
TL;DR: In this article , the authors proposed a method for automatic assignment of emotional state to learning resources, based on their feature similarity with previously labeled learning resources. But the results showed a very high value in the performance metrics, like the $$R^2$$
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Abstract: Abstract In this work, we aim to analyze the sentiments of learning resources from their textual contents. This work proposes a method for automatic assignment of emotional state to learning resources, based on their feature similarity with previously labeled learning resources. Then, various feature extraction strategies, which describe the relevant information in the texts, are compared for the task of sentiments analysis, considering the two main dimensions of emotions: arousal and valence. The results are very promising, showing a very high value in the performance metrics, like the $$R^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>R</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:math> score.
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Citations
Adaptive sentiment analysis using multioutput classification: a performance comparison
TL;DR: In this paper , a multi-output classification model for sentiment analysis through the combination of 10 algorithms: BernoulliNB, Decision Tree, K-nearest neighbor, Logistic Regression, LinearSVC, Bagging, Stacking, Random Forest, AdaBoost, and ExtraTrees.
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Assessing Teacher Competencies in Higher Education: A Sentiment Analysis of Student Feedback
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Academic Emotions in Students' Self-Regulated Learning and Achievement: A Program of Qualitative and Quantitative Research
TL;DR: In this article, taxonomies of different academic emotions and a self-report instrument measuring students' enjoyment, hope, pride, relief, anger, anxiety, shame, hopelessness, and boredom were developed.
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Steven Bird
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