Interactive methods for taxonomy editing and validation
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Introduction to Machine Learning (Adaptive Computation and Machine Learning)
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Determining the sentiment of opinions
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Truthful opinions from the crowds
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Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Micro-blogging as online word of mouth branding
CHI '09 Extended Abstracts on Human Factors in Computing Systems
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
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EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
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CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
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Student evaluation is an important component of any higher education. Online student feedbacks and opinions have become increasingly popular means of gathering reviews and judging the quality of various services offered by an institution. This paper focuses 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 that accompany the feature space. Our hypotheses is that higher the evidence of a feature in the free text, higher the quantitative numerical ratings. This study has been validated from students' feedbacks and opinions of our institution as well.