Performance Evaluation of Keyword Extraction Methods and Visualization for Student Online Comments

Topic keyword extraction (as a typical task in information retrieval) refers to extracting the core keywords from document topics. In an online environment, students often post comments in subject forums. The automatic and accurate extraction of keywords from these comments are beneficial to lecture...

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Bibliographic Details
Published in:Symmetry (Basel) Vol. 12; no. 11; p. 1923
Main Authors: Liu, Feng, Huang, Xiaodi, Huang, Weidong, Duan, Sophia Xiaoxia
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.11.2020
Subjects:
Accuracy
Algorithms
Artificial neural networks
Classification
Datasets
Decision trees
Deep learning
Indexing
Information retrieval
Keywords
Linguistics
Machine learning
Natural language processing
Neural networks
Parameter estimation
Performance evaluation
Sentiment analysis
Support vector machines
Symmetry
Visualization
ISSN:2073-8994, 2073-8994
Online Access:Get full text
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Summary:Topic keyword extraction (as a typical task in information retrieval) refers to extracting the core keywords from document topics. In an online environment, students often post comments in subject forums. The automatic and accurate extraction of keywords from these comments are beneficial to lecturers (particular when it comes to repeatedly delivered subjects). In this paper, we compare the performance of traditional machine learning algorithms and two deep learning methods in extracting topic keywords from student comments posted in subject forums. For this purpose, we collected student comment data from a period of two years, manually tagging part of the raw data for our experiments. Based on this dataset, we comprehensively compared the five typical algorithms of naïve Bayes, logistic regression, support vector machine, convolutional neural networks, and Long Short-Term Memory with Attention (Att-LSTM). The performances were measured by the four evaluation metrics. We further examined the keywords by visualization. From the results of our experiment and visualization, we conclude that the Att-LSTM method is the best approach for topic keyword extraction from student comments. Further, the results from the algorithms and visualization are symmetry, to some degree. In particular, the extracted topics from the comments posted at the same stages of different teaching sessions are, almost, reflection symmetry.
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ISSN:2073-8994
2073-8994
DOI:10.3390/sym12111923