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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 11
3283-3288

Sentiment classification of Khmer sentences based on deep semi-supervised

Li Chao1a,1b
Yan Xin1a,1b
Xu Guangyi2
Mo Yuanyuan3,4
Zhou Feng1a,1b,4
1. a. Faculty of Information Engineering & Automation, b. Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science & Technology, Kunming 650500, China
2. Yunnan Nantian Electronic Information Industry Co. , Ltd. , Kunming 650040, China
3. School of Southeast Asia & South Asia Languages & Cultures, Yunnan Minzu University, Kunming 650500, China
4. Institute of Linguistics, Shanghai Normal University, Shanghai 200234, China
DOI: 10.19734/j.issn.1001-3695.2021.04.0105

Abstract

Aiming at the problems of limited annotation data, scarce corpus and slow progress of Khmer sentence-level sentiment analysis, this paper proposed a Khmer sentence-level sentiment classification method based on deep semi-supervised CNN model. This method combined the separate convolution for word and lexicon embeddings, used a small amount of existing Khmer sentiment lexicon resources to improve sentence-level sentiment classification task performance. First, it constructed the word and lexicon embeddings of Khmer sentence, used different convolution kernels to convolve two-part embeddings respectively, integrated the existing sentiment lexicon information into the CNN model. After the max-over-time pooling, obtaining the maximum output feature. The maximum output features of the two parts were stitched together as the input of the full connection layer. And then, it used the semi-supervised learning method of temporal ensembling training the deep neural network, reduced the need for annotated corpus, and further improved the accuracy of the model's sentiment classification. The result proves that through the semi-supervised method of temporal ensembling model training, the accuracy of this method is 3.89% higher than that of the supervised method in the Khmer sentence-level sentiment classification task when the artificially labeled data is the same.

Foundation Support

国家自然科学基金资助项目(61462055,61562049)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.04.0105
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 11
Section: Algorithm Research & Explore
Pages: 3283-3288
Serial Number: 1001-3695(2021)11-014-3283-06

Publish History

[2021-11-05] Printed Article

Cite This Article

李超, 严馨, 徐广义, 等. 基于深度半监督的柬语句子级情感分类 [J]. 计算机应用研究, 2021, 38 (11): 3283-3288. (Li Chao, Yan Xin, Xu Guangyi, et al. Sentiment classification of Khmer sentences based on deep semi-supervised [J]. Application Research of Computers, 2021, 38 (11): 3283-3288. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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