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Automatic seed word selection for unsupervised sentiment classification of Chinese text

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posted on 2023-06-08, 09:37 authored by Taras Zagibalov, John Carroll
We describe and evaluate a new method of automatic seed word selection for unsupervised sentiment classification of product reviews in Chinese. The whole method is unsupervised and does not require any annotated training data; it only requires information about commonly occurring negations and adverbials. Unsupervised techniques are promising for this task since they avoid problems of domain-dependency typically associated with supervised methods. The results obtained are close to those of supervised classifiers and sometimes better, up to an F1 of 92%.

History

Publication status

  • Published

Volume

p1073-

Page range

1073-1080

Pages

8.0

Presentation Type

  • paper

Event name

22nd International Conference on Computational Linguistics (COLING)

Event location

Manchester, UK

Event type

conference

Department affiliated with

  • Informatics Publications

Full text available

  • No

Peer reviewed?

  • Yes

Legacy Posted Date

2012-02-06

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