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The Dissociation between Polarity, Semantic Orientation, and Emotional Tone as an Early Indicator of Cognitive Impairment

Overview of attention for article published in Frontiers in Computational Neuroscience, September 2016
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Title
The Dissociation between Polarity, Semantic Orientation, and Emotional Tone as an Early Indicator of Cognitive Impairment
Published in
Frontiers in Computational Neuroscience, September 2016
DOI 10.3389/fncom.2016.00095
Pubmed ID
Authors

Susana A. Arias Tapia, Rafael Martínez-Tomás, Héctor F. Gómez, Víctor Hernández del Salto, Javier Sánchez Guerrero, J. A. Mocha-Bonilla, José Barbosa Corbacho, Azizudin Khan, Veronica Chicaiza Redin

Abstract

The present study aims to identify early cognitive impairment through the efficient use of therapies that can improve the quality of daily life and prevent disease progress. We propose a methodology based on the hypothesis that the dissociation between oral semantic expression and the physical expressions, facial gestures, or emotions transmitted in a person's tone of voice is a possible indicator of cognitive impairment. Experiments were carried out with phrases, analyzing the semantics of the message, and the tone of the voice of patients through unstructured interviews in healthy people and patients at an early Alzheimer's stage. The results show that the dissociation in cognitive impairment was an effective indicator, arising from patterns of inconsistency between the analyzed elements. Although the results of our study are encouraging, we believe that further studies are necessary to confirm that this dissociation is a probable indicator of cognitive impairment.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 34 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 12%
Student > Ph. D. Student 4 12%
Lecturer > Senior Lecturer 3 9%
Other 2 6%
Student > Bachelor 2 6%
Other 3 9%
Unknown 16 47%
Readers by discipline Count As %
Psychology 4 12%
Medicine and Dentistry 3 9%
Neuroscience 2 6%
Nursing and Health Professions 1 3%
Agricultural and Biological Sciences 1 3%
Other 8 24%
Unknown 15 44%
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 14 September 2016.
All research outputs
#20,341,859
of 22,888,307 outputs
Outputs from Frontiers in Computational Neuroscience
#1,162
of 1,347 outputs
Outputs of similar age
#279,449
of 321,980 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#26
of 36 outputs
Altmetric has tracked 22,888,307 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 1,347 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.1. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 36 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.