An Improved LDA Topic Modeling Method Based on Partition for Medium and Long Texts-Reference-Cited by-同舟云学术

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An Improved LDA Topic Modeling Method Based on Partition for Medium and Long Texts

Published:2019-04-25 Issue:2 Volume:8 Page:331-344
ISSN:2198-5804
Container-title:Annals of Data Science
language:en
Short-container-title:Ann. Data. Sci.

Author:

Guo Chonghui,Lu Menglin,Wei Wei

Funder

Natural Science Foundation of China

Science and Technology Program of Jieyang

Scientific and Technological Innovation Foundation of Dalian

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Statistics, Probability and Uncertainty,Computer Science Applications,Business, Management and Accounting (miscellaneous)

Link

https://link.springer.com/content/pdf/10.1007/s40745-019-00218-3.pdf

Reference25 articles.

1. Lopez-Gazpio I, Maritxalar M, Gonzalez-Agirre A, Rigau G, Uria L, Agirre E (2017) Interpretable semantic textual similarity: finding and explaining differences between sentences. Knowl Based Syst 119:186–199

2. Wu Z, Zhu H, Li G, Cui Z, Huang H, Li J, Chen E, Xu G (2017) An efficient Wikipedia semantic matching approach to text document classification. Inf Sci 393:15–28

3. Lu M, Zhao X, Zhang L, Li F (2016) Semi-supervised concept factorization for document clustering. Inf Sci 331:86–98

4. Li W, Zhu L, Guo K et al (2018) Build a tourism-specific sentiment Lexicon via word2vec. Ann Data Sci 5(1):1–7

5. Chen Y, Yin C, Lin Y, Zuo W (2018) On-line evolutionary sentiment topic analysis modeling. Int J Comput Intell Syst 11(1):634–651

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