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Machine Learning, Optimization, and Data Science

Overview of attention for book
Machine Learning, Optimization, and Data Science
Springer International Publishing
Attention for Chapter: Can you tell? SSNet -- a Sagittal Stratum-inspired Neural Network Framework for Sentiment Analysis
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (53rd percentile)
  • Good Attention Score compared to outputs of the same age and source (74th percentile)

Mentioned by

twitter
6 X users

Readers on

mendeley
3 Mendeley
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Chapter title
Can you tell? SSNet -- a Sagittal Stratum-inspired Neural Network Framework for Sentiment Analysis
Book title
Machine Learning, Optimization, and Data Science
Published in
arXiv, February 2022
DOI 10.1007/978-3-030-95467-3_27
Book ISBNs
978-3-03-095466-6, 978-3-03-095467-3
Authors

Apostol Vassilev, Munawar Hasan, Honglan Jin, Vassilev, Apostol, Hasan, Munawar, Jin, Honglan

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Other 1 33%
Unknown 2 67%
Readers by discipline Count As %
Business, Management and Accounting 1 33%
Unknown 2 67%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 30 March 2022.
All research outputs
#14,345,282
of 24,093,053 outputs
Outputs from arXiv
#237,404
of 1,020,419 outputs
Outputs of similar age
#225,173
of 505,396 outputs
Outputs of similar age from arXiv
#7,624
of 33,165 outputs
Altmetric has tracked 24,093,053 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,020,419 research outputs from this source. They receive a mean Attention Score of 4.0. This one has gotten more attention than average, scoring higher than 74% of its peers.
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 505,396 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.
We're also able to compare this research output to 33,165 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.