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- W2592190255 abstract "Online feedbacks have become increasingly popular means of gathering students' reviews and judging the quality of various services offered by an institution, such as courses, teaching, evaluation, infrastructure and many others. Generally, academic feedbacks include values through numerical ratings and free text comments. In this paper, we employ a natural language-based approach to extract features of feedbacks, capture sentiment words from those feedbacks and build opinion vocabulary from the corpus of academic feedbacks. Also, we focus on studying student behaviour while reporting their feedbacks. Particularly, we investigate the reliability of quantitative features through numerical ratings that students offer, by estimating the linguistic evidence from the free text in the feedback." @default.
- W2592190255 created "2017-03-16" @default.
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- W2592190255 date "2013-04-01" @default.
- W2592190255 modified "2023-09-23" @default.
- W2592190255 title "Understanding Sentiment Words and Truthful Opinions from Academic Feedbacks" @default.
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