A Comparative Study of Using Bag-of-Words and Word-Embedding Attributes in the Spoiler Classification of English and Thai Text
© 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-i...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Conference or Workshop Item |
| Published: |
2020
|
| Subjects: | |
| Online Access: | https://repository.li.mahidol.ac.th/handle/123456789/49583 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Mahidol University |
| Summary: | © 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. |
|---|