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Towards Semantically Sensitive Text Clustering: A Feature Space Modeling Technology Based on Dimension Extension

Overview of attention for article published in PLOS ONE, March 2015
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Mentioned by

facebook
1 Facebook page

Citations

dimensions_citation
6 Dimensions

Readers on

mendeley
21 Mendeley
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Title
Towards Semantically Sensitive Text Clustering: A Feature Space Modeling Technology Based on Dimension Extension
Published in
PLOS ONE, March 2015
DOI 10.1371/journal.pone.0117390
Pubmed ID
Authors

Yuanchao Liu, Ming Liu, Xin Wang

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 21 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 29%
Researcher 2 10%
Student > Ph. D. Student 2 10%
Librarian 1 5%
Student > Doctoral Student 1 5%
Other 2 10%
Unknown 7 33%
Readers by discipline Count As %
Computer Science 5 24%
Psychology 2 10%
Social Sciences 2 10%
Linguistics 1 5%
Agricultural and Biological Sciences 1 5%
Other 3 14%
Unknown 7 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 March 2015.
All research outputs
#20,299,108
of 22,836,570 outputs
Outputs from PLOS ONE
#173,968
of 194,874 outputs
Outputs of similar age
#222,589
of 262,924 outputs
Outputs of similar age from PLOS ONE
#5,217
of 6,174 outputs
Altmetric has tracked 22,836,570 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 194,874 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.1. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 262,924 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 6,174 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.