Teaching Senti-Lexicon for Automated Sentiment Polarity Definition in Teaching Evaluation

Pong-inwong, Chakrit; Rungworawut, Wararat

doi:10.1109/skg.2014.25

Cited by 24 publications

(8 citation statements)

References 16 publications

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“…Although, there have been some research studies that attempted to solve this problem by using opinion mining [2], [25], [26], [29] but their work is confined to just finding the polarity of an overall sentence (i.e. positive or negative).…”

Section: Introductionmentioning

confidence: 99%

Aspect-Based Opinion Mining on Student’s Feedback for Faculty Teaching Performance Evaluation

et al. 2019

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Students' feedback is crucial for academic institutions in order to evaluate faculty performance. Handling the qualitative opinions of students efficiently while automatic report generation is a challenging task. Indeed, most organizations deal with quantitative feedback effectively, whereas qualitative feedback is either processed manually or ignored altogether. This study proposes a supervised aspect based opinion mining system based on two-layered LSTM model. The first layer predicts the aspects described within the feedback and later specifies the orientation (positive, negative, and neutral) of those predicted aspects. The model was tested on a manually tagged data set constructed from the last five years students' comments from Sukkur IBA University as well as on a standard SemEval-2014 data set. Unlike many other LSTM models proposed for other domains, the proposed model is quite simple in terms of architecture which results in less complexity. The system attains a good accuracy using the domain embedding layer in both tasks: aspect extraction (91%) and sentiment polarity detection (93%). To the best of our knowledge, this study is a first attempt that uses deep learning approach for performing aspect based sentiment analysis on students' feedback for evaluating faculty teaching performance.

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Section: Introductionmentioning

confidence: 99%

Aspect-Based Opinion Mining on Student’s Feedback for Faculty Teaching Performance Evaluation

et al. 2019

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“…However, this work still has some limitation, all the words tagged as the same polarity get the same score. Similar to [18] proposed the construction of their teaching evaluation lexicon resource. In this work, the weight score of terms as defined by the experts with the ranged from 1.00 to 1.00.…”

Section: Related Workmentioning

confidence: 99%

Combined Approach for Teachers’ Evaluation Aspects Identification Using Dictionary and Patterns Based

Kaewyong¹,

Salim²,

Phang³

2019

IJIES

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Teacher performance evaluation is a common method and often used for evaluates teaching quality in higher education. With the rapid growth of opinion mining technique. Aspect-based opinion mining application has been possibly employed to extraction and summarization of students' comments for teacher evaluation. However, to automated teacher evaluation features identification from a large number of students' comments collection is very hard work. This study has the goal to address this problem. The main objectives of the proposed method are: (1) to identify teacher evaluation aspects, (2) to compare the efficiency of dictionary based, patterns based and the combination of them, and (3) to enhance the accuracy result in the teachers’ evaluation aspects identification from the unstructured text of students' feedbacks. The students' feedbacks were collected by questionnaires and the dataset was constructed manually with a total of 4,496 sentences from 300 undergraduate student responses in 10 subjects by purposive sampling and the collection of positive and negative sentences from 30 participants group interviewed in the workshop. Both approaches were applied to identify the frequency teachers' evaluation aspects. The experimental results found that our proposed approach provided reasonably more accurate results, the combination approach enhanced a significantly average of precision and recall. For future work, we focus on the application of new linguistic patterns and non-frequency aspects in order to increase the accuracy result. Keywords—aspects identification, lexicon relation, linguistic pattern, opinion mining, teacher evaluation.

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“…Aung et al constructed a sentiment lexicon with the size of 745 words manually to analyze sentiment of students’ comment. Unfortunately, their experiments did not consider the complexity of construction in big data environments and similar problem can also be found in References [29,41]. Esparza et al presented a model called Social Mining using a corpus of real comments in Spanish about teacher performance assessment.…”

Section: Related Workmentioning

confidence: 99%

Lexical based automated teaching evaluation via students’ short reviews

Lin

Zhu

Zhang

et al. 2018

Comp Applic In Engineering

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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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Teaching Senti-Lexicon for Automated Sentiment Polarity Definition in Teaching Evaluation

Cited by 24 publications

References 16 publications

Aspect-Based Opinion Mining on Student’s Feedback for Faculty Teaching Performance Evaluation

Aspect-Based Opinion Mining on Student’s Feedback for Faculty Teaching Performance Evaluation

Combined Approach for Teachers’ Evaluation Aspects Identification Using Dictionary and Patterns Based

Lexical based automated teaching evaluation via students’ short reviews

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