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Student evaluations of teaching (SET) have become a popular approach to assess faculties’ teaching. Question‐score‐based questionnaire is the most common SET measure adopted in universities. However, it fails to cover important facets of teaching process that not mentioned in the predefined questionnaire, which can be substantially obtained from students’ short reviews. In this paper, we propose two lexical‐based methods, specifically knowledge‐based and machine learning‐based, to automatically extract opinions from short reviews. Furthermore, the diversity of reviews’ themes and styles of same sentiment polarity reviews can be observed from the extracted opinion results. The experimental results show that the proposed methods are able to achieve accuracies of 78.13 and 84.78%, respectively in the task of student review sentiment classification. Further investigation on linguistic features shows that reviews with same sentiment polarity shares similar language patterns. Finally, we present an application scenario in real SET process by utilizing aforementioned methods and discoveries.

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Underwater dam crack detection and classification based on visible images is a challenging task. The underwater environment is very complex with uneven illumination and serious noise problems, which often leads to the distortion of detection. In addition, there are few methods suitable for underwater dam crack classification. To solve these problems, a novel underwater dam crack detection and classification approach is proposed. Firstly, a dodging algorithm is used to eliminate the uneven illumination in the underwater visible images. Subsequently, a crack detection approach is proposed, where the local characteristics of image blocks and the global characteristics of connected domains are both used based on the analysis of the statistical properties of dam crack images. Finally, an improved evidence theory-based crack classification algorithm is proposed after the crack detection. Experimental results show that the proposed approach is able to detect underwater dam cracks and classify them accurately and effectively in complex underwater environments.

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