Keyword extraction algorithms for emotion recognition from Uyghur text

doi:10.16511/j.cnki.qhdxxb.2017.26.008

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Abstract This paper describes sentiment classification research on Uyghur text using different keyword extraction methods to recognize common emotions like anger and happiness. The keywords expressing happiness and anger are extracted using the TextRank, sparse discriminant analysis (SDA) and sparse support vector machine (Sparse SVM) methods to train feature extraction and sentiment models. A sentiment text database was built by excerpting the anger and happiness sentiments from Uyghur movies and novels with several validation experiments based on those text databases. The tests show that the keyword extraction methods presented in this paper are effective for emotion classification from Uyghur sentences. The Sparse SVM method is robustness and has higher accuracy in recognition tests with a smaller number of keywords extracted.

Keywords TextRank sparse discriminant analysis(SDA) sparse support vector machine(Sparse SVM) emotion recognition Uyghur

ZTFLH:

TP391.1

Issue Date: 15 March 2017

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Cite this article:

IMAM Seyyare, PARHAT Rayilam, HAMDULLA Askar, LI Zhijun. Keyword extraction algorithms for emotion recognition from Uyghur text[J]. Journal of Tsinghua University(Science and Technology),2017, 57(3): 270-273.

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http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2017.26.008 OR http://jst.tsinghuajournals.com/EN/Y2017/V57/I3/270

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