analytical tools for emojis, textual expressions of emotions?
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@media.uio.no
Dear Charles, I'll send you a couple of references, which may be of your student's interest. Sampietro, A., & Valera. (2015). Emotional politics on Facebook. An exploratory study of Podemos’ discourse during the European election campaign 2014. Recerca. Revista de pensament i anàlisi, 17. Available at: http://www.e-revistes.uji.es/index.php/recerca/article/view/1739/1584 Anderson, A. A., Brossard, D., Scheufele, D. A., Xenos, M. A., & Ladwig, P. (2014). The “Nasty Effect:” Online Incivility and Risk Perceptions of Emerging Technologies. Journal of Computer-Mediated Communication, 19(3), 373-387. http://doi.org/10.1111/jcc4.12009 Barbieri, F., Ronzano, F., & Saggion, H. (2016, May). What does this Emoji Mean? A Vector Space Skip-Gram Model for Twitter Emojis. In *LREC*. Kind regards, Agnese Sampietro <http://www.linkedin.com/in/agnesesampietro> http://www.linkedin.com/in/agnesesampietro @speakabouttech <https://twitter.com/speakabouttech> https://sites.google.com/view/agnesesampietro 2018-06-11 12:44 GMT+02:00 Charles M. Ess <c.m.ess@media.uio.no>:
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@media.uio.no _______________________________________________ The Air-L@listserv.aoir.org mailing list is provided by the Association of Internet Researchers http://aoir.org Subscribe, change options or unsubscribe at: http://listserv.aoir.org/listinfo.cgi/air-l-aoir.org
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This may also be of use: https://unicode.org/emoji/charts/full-emoji-list.html All of the emoji's can be used as capture rules via the Premium Twitter API (formerly Gnip). ~Stu On Mon, Jun 11, 2018 at 7:24 AM, Agnese Sampietro <agsamp@gmail.com> wrote:
Dear Charles,
I'll send you a couple of references, which may be of your student's interest.
Sampietro, A., & Valera. (2015). Emotional politics on Facebook. An exploratory study of Podemos’ discourse during the European election campaign 2014. Recerca. Revista de pensament i anàlisi, 17. Available at: http://www.e-revistes.uji.es/index.php/recerca/article/view/1739/1584
Anderson, A. A., Brossard, D., Scheufele, D. A., Xenos, M. A., & Ladwig, P. (2014). The “Nasty Effect:” Online Incivility and Risk Perceptions of Emerging Technologies. Journal of Computer-Mediated Communication, 19(3), 373-387. http://doi.org/10.1111/jcc4.12009
Barbieri, F., Ronzano, F., & Saggion, H. (2016, May). What does this Emoji Mean? A Vector Space Skip-Gram Model for Twitter Emojis. In *LREC*.
Kind regards,
Agnese Sampietro <http://www.linkedin.com/in/agnesesampietro> http://www.linkedin.com/in/agnesesampietro @speakabouttech <https://twitter.com/speakabouttech> https://sites.google.com/view/agnesesampietro
2018-06-11 12:44 GMT+02:00 Charles M. Ess <c.m.ess@media.uio.no>:
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@media.uio.no _______________________________________________ The Air-L@listserv.aoir.org mailing list is provided by the Association of Internet Researchers http://aoir.org Subscribe, change options or unsubscribe at: http://listserv.aoir.org/listinfo.cgi/air-l-aoir.org
Join the Association of Internet Researchers: http://www.aoir.org/
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-- Dr. Stuart W. Shulman Founder and CEO, Texifter Cell: 413-992-8513 LinkedIn: http://www.linkedin.com/in/stuartwshulman
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@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@media.uio.no
Hi Charles, To help with some student projects, I patched together an almost ridiculously primitive tool, using Python's emoji package - you paste text in a form field and get emoji statistics in return: http://labs.polsys.net/tools/textanalysis/ May still be useful somehow. best, Bernhard --- Bernhard Rieder | Associate Professor | New Media and Digital Culture University of Amsterdam | Turfdraagsterpad 9 | 1012 XT Amsterdam | The Netherlands http://thepoliticsofsystems.net | http://labs.polsys.net | https://www.digitalmethods.net | @RiederB
On 11 Jun 2018, at 11:44, Charles M. Ess <c.m.ess@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@media.uio.no _______________________________________________ The Air-L@listserv.aoir.org mailing list is provided by the Association of Internet Researchers http://aoir.org Subscribe, change options or unsubscribe at: http://listserv.aoir.org/listinfo.cgi/air-l-aoir.org
Join the Association of Internet Researchers: http://www.aoir.org/
participants (5)
-
Agnese Sampietro -
Bernhard Rieder -
Cecilia Aragon -
Charles M. Ess -
Shulman, Stu