[Air-L] emotion detection machine?

[Aysenur Ataman]

Dear Charles,

You make a great point about categories of emotions at the end of your email. Scholars interested in emotions have many theories and ways of categorizing humans’ affective states (positive vs. negative; primary vs. secondary, etc.). First, I would urge your student to investigate different classifications of emotions and humans ways of expressing them. By jumping ahead this step and seeking commercial tools (sometimes with no solid background and clarification), we miss the chance of grounding our findings and reporting reliable analysis. I find Significance Analysis (Daiute, 2014)<https://uk.sagepub.com/en-gb/eur/narrative-inquiry/book240491> based on Labov and Waletzky (1997)<http://Labov,%20W.,%20&%20Waletzky,%20J.%20(1997).%20Narrative%20analysis:%20Oral%20versions%20of%20personal%20experience.%20Journal%20of%20Narrative%20&%20Life%20History,%207(1-4),%203-38.> work particularly useful in combining importance of linguistic elements and psychological states.

Based on those readings, motivate your student to read the textual data (comments) repeatedly and come up with their own categorization. This step allows the researcher to be mindful about the contextually significant elements of the textual data. What clues can be used to infer the emotional tone of the comment? Ability to use R after this step would be especially useful because programming skills would allow the student to be flexible and not dependent on existing categories the above mentioned tools use.

Jess suggested LICW. And Stu suggested Discover Text. Those two would be my only recommendations if researcher-led analysis is not possible for now.

Lastly, congrats for caring so much about your students’ research idea and seeking help for them!

_____________________________________________
Ayşenur Benevento
Faculty of Humanities and Social Sciences
Abdullah Gül University
On 5 Sep 2019 16:46 +0300, Jessica Pater <jesspater at gmail.com>, wrote:
Charles,

Another tool you might want to look into is LIWC http://liwc.wpengine.com/.

Jess

On Thu, Sep 5, 2019 at 6:52 AM Charles M. Ess <c.m.ess at media.uio.no> wrote:

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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--
Jessica A. Pater
PhD Candidate, Human Centered Computing
Georgia Institute of Technology
pater at gatech.edu
www.jesspater.com
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