| Title |
Sentiment analysis methods for understanding large-scale texts: a case for using continuum-scored words and word shift graphs
|
|---|---|
| Published in |
EPJ Data Science, October 2017
|
| DOI | 10.1140/epjds/s13688-017-0121-9 |
| Authors |
Andrew J Reagan, Christopher M Danforth, Brian Tivnan, Jake Ryland Williams, Peter Sheridan Dodds |
X Demographics
The data shown below were collected from the profiles of 103 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 37 | 36% |
| Italy | 5 | 5% |
| Germany | 4 | 4% |
| Spain | 3 | 3% |
| France | 3 | 3% |
| Canada | 3 | 3% |
| United Kingdom | 2 | 2% |
| Australia | 2 | 2% |
| Indonesia | 2 | 2% |
| Other | 7 | 7% |
| Unknown | 35 | 34% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 56 | 54% |
| Scientists | 41 | 40% |
| Science communicators (journalists, bloggers, editors) | 4 | 4% |
| Practitioners (doctors, other healthcare professionals) | 2 | 2% |
Mendeley readers
The data shown below were compiled from readership statistics for 137 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | <1% |
| Unknown | 136 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 29 | 21% |
| Student > Master | 16 | 12% |
| Researcher | 14 | 10% |
| Student > Doctoral Student | 9 | 7% |
| Student > Bachelor | 7 | 5% |
| Other | 21 | 15% |
| Unknown | 41 | 30% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 35 | 26% |
| Social Sciences | 13 | 9% |
| Business, Management and Accounting | 5 | 4% |
| Engineering | 5 | 4% |
| Linguistics | 5 | 4% |
| Other | 27 | 20% |
| Unknown | 47 | 34% |
Attention Score in Context
This research output has an Altmetric Attention Score of 83. 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 29 January 2021.
All research outputs
#508,095
of 25,238,182 outputs
Outputs from EPJ Data Science
#31
of 433 outputs
Outputs of similar age
#10,805
of 335,657 outputs
Outputs of similar age from EPJ Data Science
#3
of 8 outputs
Altmetric has tracked 25,238,182 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 433 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 39.5. This one has done particularly well, scoring higher than 93% 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 335,657 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 96% of its contemporaries.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.