[Air-L] emotion detection machine?

[Arin Basu]

You can also do sentiment analysis in R using quanteda() package and
using your own dictionary or their lexicoder sentiment dictionary
(https://quanteda.io/reference/data_dictionary_LSD2015.html),
sentimentr is another good package. The advantage of using quanteda is
the shorter lines of code, so you do something.

If you use natural language processing, a good idea is to use spacy in
python but you can also use spacyr in the quanteda library. CoreNLP is
an excellent toolkit in Java, and cleanNLP package in R that you can
use in quanteda or tidytext is excellent.

Tidytext is very nice and it helps you to play with AFINN, NRC, Bing
(Bing Liu's lexicon) but these have three different ways of
classifying emotions and sentiments. If you install syuzhet() and call
the library in an R workspace, it helps to download the sentiment
lexicons in tidytext using the get_sentiment() function is how I have
seen.


On Fri, 6 Sep 2019 at 01:41, Maurice Vergeer <m.vergeer at maw.ru.nl> wrote:
>
> 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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> --
> ________________________________________________
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