@inproceedings{wang-ku-2016-antusd,
title = "{ANTUSD}: A Large {C}hinese Sentiment Dictionary",
author = "Wang, Shih-Ming and
Ku, Lun-Wei",
booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
month = may,
year = "2016",
address = "Portoro{\v{z}}, Slovenia",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L16-1428",
pages = "2697--2702",
abstract = "This paper introduces the augmented NTU sentiment dictionary, abbreviated as ANTUSD, which is constructed by collecting sentiment stats of words in several sentiment annotation work. A total of 26,021 words were collected in ANTUSD. For each word, the CopeOpi numerical sentiment score and the number of positive annotation, neutral annotation, negative annotation, non-opinionated annotation, and not-a-word annotation are provided. Words and their sentiment information in ANTUSD have been linked to the Chinese ontology E-HowNet to provide rich semantic information. We demonstrate the usage of ANTUSD in polarity classification of words, and the results show that a superior f-score 98.21 is achieved, which supports the usefulness of the ANTUSD. ANTUSD can be freely obtained through application from NLPSA lab, Academia Sinica: http://academiasinicanlplab.github.io/",
}
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%0 Conference Proceedings
%T ANTUSD: A Large Chinese Sentiment Dictionary
%A Wang, Shih-Ming
%A Ku, Lun-Wei
%S Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)
%D 2016
%8 may
%I European Language Resources Association (ELRA)
%C Portorož, Slovenia
%F wang-ku-2016-antusd
%X This paper introduces the augmented NTU sentiment dictionary, abbreviated as ANTUSD, which is constructed by collecting sentiment stats of words in several sentiment annotation work. A total of 26,021 words were collected in ANTUSD. For each word, the CopeOpi numerical sentiment score and the number of positive annotation, neutral annotation, negative annotation, non-opinionated annotation, and not-a-word annotation are provided. Words and their sentiment information in ANTUSD have been linked to the Chinese ontology E-HowNet to provide rich semantic information. We demonstrate the usage of ANTUSD in polarity classification of words, and the results show that a superior f-score 98.21 is achieved, which supports the usefulness of the ANTUSD. ANTUSD can be freely obtained through application from NLPSA lab, Academia Sinica: http://academiasinicanlplab.github.io/
%U https://aclanthology.org/L16-1428
%P 2697-2702
Markdown (Informal)
[ANTUSD: A Large Chinese Sentiment Dictionary](https://aclanthology.org/L16-1428) (Wang & Ku, LREC 2016)
ACL
- Shih-Ming Wang and Lun-Wei Ku. 2016. ANTUSD: A Large Chinese Sentiment Dictionary. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 2697–2702, Portorož, Slovenia. European Language Resources Association (ELRA).