@inproceedings{ali-etal-2021-creating,
title = "Creating and Evaluating Resources for Sentiment Analysis in the Low-resource Language: {S}indhi",
author = "Ali, Wazir and
Ali, Naveed and
Dai, Yong and
Kumar, Jay and
Tumrani, Saifullah and
Xu, Zenglin",
editor = "De Clercq, Orphee and
Balahur, Alexandra and
Sedoc, Joao and
Barriere, Valentin and
Tafreshi, Shabnam and
Buechel, Sven and
Hoste, Veronique",
booktitle = "Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wassa-1.20",
pages = "188--194",
abstract = "In this paper, we develop Sindhi subjective lexicon using a merger of existing English resources: NRC lexicon, list of opinion words, SentiWordNet, Sindhi-English bilingual dictionary, and collection of Sindhi modifiers. The positive or negative sentiment score is assigned to each Sindhi opinion word. Afterwards, we determine the coverage of the proposed lexicon with subjectivity analysis. Moreover, we crawl multi-domain tweet corpus of news, sports, and finance. The crawled corpus is annotated by experienced annotators using the Doccano text annotation tool. The sentiment annotated corpus is evaluated by employing support vector machine (SVM), recurrent neural network (RNN) variants, and convolutional neural network (CNN).",
}
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%0 Conference Proceedings
%T Creating and Evaluating Resources for Sentiment Analysis in the Low-resource Language: Sindhi
%A Ali, Wazir
%A Ali, Naveed
%A Dai, Yong
%A Kumar, Jay
%A Tumrani, Saifullah
%A Xu, Zenglin
%Y De Clercq, Orphee
%Y Balahur, Alexandra
%Y Sedoc, Joao
%Y Barriere, Valentin
%Y Tafreshi, Shabnam
%Y Buechel, Sven
%Y Hoste, Veronique
%S Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F ali-etal-2021-creating
%X In this paper, we develop Sindhi subjective lexicon using a merger of existing English resources: NRC lexicon, list of opinion words, SentiWordNet, Sindhi-English bilingual dictionary, and collection of Sindhi modifiers. The positive or negative sentiment score is assigned to each Sindhi opinion word. Afterwards, we determine the coverage of the proposed lexicon with subjectivity analysis. Moreover, we crawl multi-domain tweet corpus of news, sports, and finance. The crawled corpus is annotated by experienced annotators using the Doccano text annotation tool. The sentiment annotated corpus is evaluated by employing support vector machine (SVM), recurrent neural network (RNN) variants, and convolutional neural network (CNN).
%U https://aclanthology.org/2021.wassa-1.20
%P 188-194
Markdown (Informal)
[Creating and Evaluating Resources for Sentiment Analysis in the Low-resource Language: Sindhi](https://aclanthology.org/2021.wassa-1.20) (Ali et al., WASSA 2021)
ACL