[Air-L] analytical tools for emojis, textual expressions of emotions?

[Cecilia Aragon]

Dear Charles,

Our group has studied emotion and emoticons in text and produced several
open source analysis tools (https://depts.washington.edu/hdsl/tools/).


   - "Statistical Affect Detection in Collaborative Chat,"
   <http://faculty.washington.edu/aragon/pubs/CSCW2013.pdf> Michael Brooks,
   Katie Kuksenok, Megan Torkildson, Daniel Perry, John Robinson, Paul Harris,
   Ona Anicello, Taylor Scott, Ariana Zukowski, Cecilia Aragon. *Proceedings
   of the ACM Conference on Computer Supported Cooperative Work, CSCW '13,* San
   Antonio, TX (2013)
   - "Collaborative Visual Analysis of Sentiment in Twitter Events,"
   <http://faculty.washington.edu/aragon/pubs/cdve2014_brooks.pdf> Michael
   Brooks, John J. Robinson, Megan K. Torkildson, Sungsoo (Ray) Hong, Cecilia
   R. Aragon. International Conference on Cooperative Design, Visualization, &
   Engineering (2014).


Best regards,
Cecilia

--
Cecilia R. Aragon, Professor
Department of Human Centered Design & Engineering, University of Washington
Director, Human Centered Data Science Lab
Senior Data Science Fellow, eScience Institute
407A Sieg Hall, Box 352315, Seattle, WA 98195 USA
http://faculty.washington.edu/aragon
@craragon

On Mon, Jun 11, 2018 at 3:44 AM, Charles M. Ess <c.m.ess at media.uio.no>
wrote:

> Dear AoIRists,
>
> one of our MA students is exploring the role of diverse emotions - first
> of all, anger - in responses to news stories on three major Norwegian news
> sites.  The broad hypothesis is that anger will be most prevalent and
> effective in catalyzing further response (including sharing, likes, etc.) -
> but four emotions total are taken on board: sadness, anger, surprise, and
> happiness.
>
> 1) There is an online tool available for analyzing the emotive content of
> texts - "The SATI API enables to perform Sentiment Analysis from Textual
> Information", etc.  Comments and observations on its utility, validity?
>
> 2) Recommendations, please, for either useful examples of similar
> research, especially with a view towards methods of accumulating and then
> analyzing emoticons, and/or other suggestions regarding possible tools?
>
> Many thanks in advance,
> - charles ess
> --
> Professor in Media Studies
> Department of Media and Communication
> University of Oslo
> <http://www.hf.uio.no/imk/english/people/aca/charlees/index.html>
>
> Postboks 1093
> Blindern 0317
> Oslo, Norway
> c.m.ess at media.uio.no
>