A Sentiment Analysis Model to Analyze Students Reviews of Teacher Performance Using Support Vector Machines

Esparza, Guadalupe Gutiérrez; de-Luna, Alejandro; Zezzatti, Alberto Ochoa; Hernández, Alberto; Julio, Ponce; Álvarez, Marco; Cossío, Edgar; Nava, José

doi:10.1007/978-3-319-62410-5_19

Cited by 38 publications

(28 citation statements)

References 8 publications

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“…To alleviate this problem, [1] proposed the use of sentiment analysis techniques to identify the students' feelings. Later, [2][3][4] also tried to solve the same issue. The work of [3] is the closest to our case in terms of geographic location, language, and regulations of higher education.…”

Section: Related Workmentioning

confidence: 99%

“…Hence, this research aims to analyze the application of sentiment analysis for documents with mixed languages. The Support Vector Machine (SVM) was used as the classification method for it was found to yield more accurate result within this particular field as reported in [2][3][4].…”

Section: Introductionmentioning

confidence: 97%

“…This analysis will become a tiresome tasks when there are hundreds or even thousands of them. To alleviate this problem there are efforts of using computer-assisted classfications, such machine learning, as discussed in [1][2][3][4]. However, these studies only incorporated single language.…”

Section: Introductionmentioning

confidence: 99%

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Implementing Support Vector Machine Sentiment Analysis to Students' Opinion toward Lecturer in an Indonesian Public University

Sengkey

Jacobus

Manoppo

2019

JoSEPS

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Student feedback is an important evaluation tool for quality improvement. Moreover, in Indonesia's higher education system there is an assessment regulation that puts special attention to the availability of the student feedback system. However, parts of the questionnaire are in the form of descriptive text that requires more effort for analysis. This situation leads to a very tiresome work in case of the number of documents reaches several hundred or even thousands. There were some efforts to apply computer-assisted classification by utilizing machine learning, however, most of them only analyzed English documents. Only a handful that studied the classification of documents in Bahasa Indonesia. In reality, we found some cases where the students used mixed languages while filling the evaluation forms. Therefore, in this study, we expand the application of text classification by using Support Vector Machne (SVM) to cases of student feedback in mixed languages. The model was built computationally and from the test, we get 74% accuracy and 0.46 Kappa value.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

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Implementing Support Vector Machine Sentiment Analysis to Students' Opinion toward Lecturer in an Indonesian Public University

Sengkey

Jacobus

Manoppo

2019

JoSEPS

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“…It is also used for performance measurement and implementation of constructive feedback and revise strategies in order to benefit students in their learning process (Blau, Shamir-Inbal, 2017). The opinion and feedback given by students has become one of the most important determinants of consideration when evaluating lecturers or academicians (Esparza et al, 2018). Collins et al (2017) in their research identified benefits of undergraduate research participation for university students.…”

Section: Theoretical Background Of the Workmentioning

confidence: 99%

Preferable Course of Study and Learning Support by Business University Students

Vnoučková

Urbancová

2019

Acta Univ. Agric. Silvic. Mendelianae Brun.

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Assessment of higher education is a multifaceted and multidimensional concept. Therefore this paper focuses on assessment of preferences by business university students. The aim of this paper is to identify preferable course of study and benefits of business higher education in case private business university. The paper shows how students perceive and evaluate study support by modern information and communication technologies and identifies most important areas of their preferences during studies. The results are based on a quantitative survey by questionnaire data collection from all students of surveyed Business University. Descriptive statistics, correlation analyisis, analysis of differences and factor analysis was conducted to find responses from students regarding their perception of the educational process. The analysis found groups of variables with significant appearance of students’ main preferences. The results show orientation of students on benefits of university education mostly in career development (p = 0.000). Prefered curiculum of graduates most importantly contains independent work, significant position at job market and self management. In the area of study support students mostly prefer flexibility and availability of study texts and materials online using mobile applications (Cramer’s V = 0.314, p = 0.013). Other significant group of students focus on expert teachers and knowledge and online / mobile consultaions. The results may help to set up students’ support in order to address current students’ needs and expectations.

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“…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. They used different SVM models and acquired a relative high performance with accuracy of 81.49%.…”

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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A Sentiment Analysis Model to Analyze Students Reviews of Teacher Performance Using Support Vector Machines

Cited by 38 publications

References 8 publications

Implementing Support Vector Machine Sentiment Analysis to Students' Opinion toward Lecturer in an Indonesian Public University

Implementing Support Vector Machine Sentiment Analysis to Students' Opinion toward Lecturer in an Indonesian Public University

Preferable Course of Study and Learning Support by Business University Students

Lexical based automated teaching evaluation via students’ short reviews

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