Comparing machine learning algorithms to predict topic keywords of student comments

Feng Liu; Xiaodi Huang; Weidong Huang

doi:10.1007/978-3-030-60816-3_20

Comparing machine learning algorithms to predict topic keywords of student comments

Feng Liu, Xiaodi Huang, Weidong Huang

Research output: Book chapter/Published conference paper › Chapter (peer-reviewed) › peer-review

1 Citation (Scopus)

Abstract

Student comments as a kind of online teaching feedback in higher education organizations are becoming important which provides the evidence to improve the quality of teaching and learning. Effectively extracting useful information from the comments is critical. On the other hand, machine learning algorithms have achieved great performance in automatically extracting information and making predictions. This research compared the performance of three statistical machine learning algorithms and two deep learning methods on topic keyword extraction.

Original language	English
Title of host publication	Cooperative design, visualization, and engineering
Editors	Yuhua Luo
Place of Publication	Cham, Switzerland
Publisher	Springer
Chapter	18
Pages	178-183
Number of pages	6
ISBN (Electronic)	9783030608163
ISBN (Print)	9783030608156
DOIs	https://doi.org/10.1007/978-3-030-60816-3_20
Publication status	E-pub ahead of print - 16 Oct 2020
Event	17th International Conference on Cooperative Design, Visualization and Engineering - Duration: 25 Oct 2020 → 28 Oct 2020

Publication series

Name	Lecture Notes in Computer Science
Volume	12341

Conference

Conference	17th International Conference on Cooperative Design, Visualization and Engineering
Period	25/10/20 → 28/10/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/978-3-030-60816-3_20

Cite this

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title = "Comparing machine learning algorithms to predict topic keywords of student comments",

abstract = "Student comments as a kind of online teaching feedback in higher education organizations are becoming important which provides the evidence to improve the quality of teaching and learning. Effectively extracting useful information from the comments is critical. On the other hand, machine learning algorithms have achieved great performance in automatically extracting information and making predictions. This research compared the performance of three statistical machine learning algorithms and two deep learning methods on topic keyword extraction.",

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author = "Feng Liu and Xiaodi Huang and Weidong Huang",

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doi = "10.1007/978-3-030-60816-3_20",

language = "English",

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}

Liu, F, Huang, X & Huang, W 2020, Comparing machine learning algorithms to predict topic keywords of student comments. in Y Luo (ed.), Cooperative design, visualization, and engineering. Lecture Notes in Computer Science, vol. 12341, Springer, Cham, Switzerland, pp. 178-183, 17th International Conference on Cooperative Design, Visualization and Engineering, 25/10/20. https://doi.org/10.1007/978-3-030-60816-3_20

Comparing machine learning algorithms to predict topic keywords of student comments. / Liu, Feng; Huang, Xiaodi; Huang, Weidong.
Cooperative design, visualization, and engineering. ed. / Yuhua Luo. Cham, Switzerland: Springer, 2020. p. 178-183 (Lecture Notes in Computer Science; Vol. 12341).

Research output: Book chapter/Published conference paper › Chapter (peer-reviewed) › peer-review

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AU - Liu, Feng

AU - Huang, Xiaodi

AU - Huang, Weidong

N1 - Includes bibliographical references.

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Y1 - 2020/10/16

N2 - Student comments as a kind of online teaching feedback in higher education organizations are becoming important which provides the evidence to improve the quality of teaching and learning. Effectively extracting useful information from the comments is critical. On the other hand, machine learning algorithms have achieved great performance in automatically extracting information and making predictions. This research compared the performance of three statistical machine learning algorithms and two deep learning methods on topic keyword extraction.

AB - Student comments as a kind of online teaching feedback in higher education organizations are becoming important which provides the evidence to improve the quality of teaching and learning. Effectively extracting useful information from the comments is critical. On the other hand, machine learning algorithms have achieved great performance in automatically extracting information and making predictions. This research compared the performance of three statistical machine learning algorithms and two deep learning methods on topic keyword extraction.

KW - Student comments

KW - Machine learning

KW - Topic keywords extraction

KW - Naïve bayes

KW - LogR

KW - SVM

KW - CNN

KW - Att-LSTM

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T2 - 17th International Conference on Cooperative Design, Visualization and Engineering

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ER -

Comparing machine learning algorithms to predict topic keywords of student comments

Abstract

Publication series

Conference

UN SDGs

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