[Air-L] emotion detection machine?

[Maurice Vergeer]

dear Charles and student

there an R package called syuzhet which has sentiment lexicons included.
Another option is to use the R-package tidytext which includes the
NRC-lexicon. The manuals provide some examples on how to use it. The
drawback is, you need to have experience with R or learn it. It's not a
waste because R can do a lot more than sentiment analysis.
These packages and R are for free.
see:
-
https://cran.r-project.org/web/packages/syuzhet/vignettes/syuzhet-vignette.html
- https://cran.r-project.org/web/packages/tidytext/vignettes/tidytext.html

hope this helps
best Maurice



Op do 5 sep. 2019 om 12:53 schreef Charles M. Ess <c.m.ess at media.uio.no>:

> Dear colleagues,
>
> One of our students is wanting to analyze emotional content in in the
> comment fields of a major newspaper vis-a-vis specific hot-button issues.
>
> She has a good tool (I think) for scrapping the data - but she is
> stymied over the choice of an emotion analysis tool. She has looked at
> Senpy (http://senpy.gsi.upm.es/#test) and  Twinword
> <https://www.twinword.com/api/emotion-analysis.php> - the latter seems
> the most accurate, but it is also expensive.
> She has recently discovered DepecheMood emotion lexicons (Staiano, J., &
> Guerini, M. (2014). Depechemood: a lexicon for emotion analysis from
> crowd-annotated news. arXiv preprint arXiv:1405.1605.) - but this
> suffers from a lack of clarity in terms of explaining its emotional
> categories: awe, indifference, sad, amusement , annoyance, joy, fear and
> anger.
>
> For my part, I am entirely clueless.  Any suggestions that she might
> pursue would be greatly appreciated.
>
> best,
> - 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
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