Publication:
A Comparative Study of Using Bag-of-Words and Word-Embedding Attributes in the Spoiler Classification of English and Thai Text

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Issued Date

2020-01-01

Resource Type

Conference Paper

ISSN

18609503
1860949X

DOI

10.1007/978-3-030-25217-5_7

Other identifier(s)

2-s2.0-85077127495

Rights

Mahidol University

Rights Holder(s)

SCOPUS

Bibliographic Citation

Studies in Computational Intelligence. Vol.847, (2020), 81-93

Suggested Citation

Rangsipan Marukatat A Comparative Study of Using Bag-of-Words and Word-Embedding Attributes in the Spoiler Classification of English and Thai Text. Studies in Computational Intelligence. Vol.847, (2020), 81-93. doi:10.1007/978-3-030-25217-5_7 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/49583

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Title

A Comparative Study of Using Bag-of-Words and Word-Embedding Attributes in the Spoiler Classification of English and Thai Text

Author(s)

Rangsipan Marukatat

Other Contributor(s)

Mahidol University

Abstract

© 2020, Springer Nature Switzerland AG. This research compares the effectiveness of using traditional bag-of-words and word-embedding attributes to classify movie comments into spoiler or non-spoiler. Both approaches were applied to comments in English, an inflectional language; and in Thai, a non-inflectional language. Experimental results suggested that in terms of classification performance, word embedding was not clearly better than bag of words. Yet, a decision to choose it over bag of words could be due to its scalability. Between Word2Vec and FastText embeddings, the former was favorable when few out-of-vocabulary (OOV) words were present. Finally, although FastText was expected to be helpful with a large number of OOV words, its benefit was hardly seen for Thai language.

Keyword(s)

Computer Science

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URI

https://repository.li.mahidol.ac.th/handle/123456789/49583

Collections

Scopus 2020