<p>I would search under the topic of <a href="http://en.wikipedia.org/wiki/Affective_computing" rel="nofollow">affective computing</a> especially in <em>detecting and recognizing emotional information</em> and then specialize on <em>NLP</em> methods in this area.</p>

<p>Here are a few articles of interest:</p>

<p><a href="http://larifari.org/_/writing/CHI2003-PoseidonsEye.pdf" rel="nofollow">Visualizing the affective structure of a text document</a></p>

<p><a href="http://larifari.org/_/writing/IUI2003-AffectSensing.pdf" rel="nofollow">A model of textual affect sensing using real-world knowledge</a></p>

<p><a href="http://larifari.org/_/writing/AAAICompAesthetics2006-Saurus.pdf" rel="nofollow">Saurus: an emotionally-weighted thesaurus</a></p>

<p><hr>
References:</p>

<p>Gouldstone, J., Liu, H., Lieberman, H., Ishii, H. (2006). Saurus: an emotionally-weighted thesaurus.  <em>Computational aesthetics: Artificial intelligence approaches to beauty and happiness</em>, Technical Report WS-06-04.</p>

<p>Liu, H., Selker, T., Lieberman, H. (2003). Visualizing the Affective Structure of a Text Document. <em>Proceedings of the Conference on Human Factors in Computing Systems</em>, CHI 2003, April 5-10, 2003, Ft. Lauderdale, FL, USA. ACM 2003, ISBN 1-58113-637-4, pp. 740-741.</p>

<p>Liu, H., Lieberman, H., Selker, T. (2003). A Model of Textual Affect Sensing using Real-World Knowledge. <em>Proceedings of the 2003 International Conference on Intelligent User Interfaces</em>, IUI 2003, January 12-15, 2003, Miami, FL, USA. ACM 2003, ISBN 1-58113-586-6, pp. 125-132. Miami, Florida.</p>

CC BY-SA 3.0
2012-03-28T19:45:07.897Z
2012-10-20T09:49:17.743Z
2012-10-20T09:49:17.743Z
xralf
<p>The field that is doing this work you describe is <a href="http://en.wikipedia.org/wiki/Sentiment_analysis">sentiment analysis</a>.  From Wikipedia:</p>

<blockquote>
  <p>A basic task in sentiment analysis is classifying the polarity of a given text at the document, sentence, or feature/aspect level — whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral. Advanced, "beyond polarity" sentiment classification looks, for instance, at emotional states such as "angry," "sad," and "happy."</p>
</blockquote>

<p>From <a href="http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html">an introductory course</a> (lots of good references on this page)</p>

<blockquote>
  <p>This work is in the general area of sentiment analysis, opinion extraction or opinion mining, and feature-based opinion summarization from the user-generated content or user-generated media on the Web, e.g., reviews, forum and group discussions, and blogs. In our <a href="http://www.cs.uic.edu/~liub/publications/kdd04-revSummary.pdf">KDD-2004</a> (Minqing Hu and Bing Liu. "Mining and summarizing customer reviews." Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining, Seattle, Washington, USA, Aug 22-25, 2004.) paper, we proposed the Feature-Based Opinion Mining model, which is now also called Aspect-Based Opinion Mining (as the term feature here can confuse with the term feature used in machine learning). The output of such opinion mining is a feature-based opinion summary or aspect-based opinion summary. The area is also related to sentiment classification. Our current work is in two main areas, which reflect two kinds of opinions (or evaluations)</p>
  
  <ul>
  <li>Mining regular (or direct) opinions. Ex: (1). This camera is great. (2). After taking the drug, I got stomach pain.</li>
  <li>Mining comparative opinions. Ex: Coke tastes better than Pepsi.</li>
  </ul>
</blockquote>

<p>In terms of your actual goals, scroll down to <a href="http://nlp-s11.utcompling.com/assignments/sentiment-analysis">extra credit</a> on this page, and you'll find an exercise that goes through a scenario close to what you describe with emoticons.</p>

<p>In terms of peer-reviewed, Chmiel A, Sienkiewicz J, et al (2011). Collective emotions online and their influence on community life, PLoS One, 6(7), e22207. <a href="http://www.plosone.org/article/fetchObjectAttachment.action;jsessionid=632B1932DB1134A8B5CD898D126C551B?uri=info%3Adoi%2F10.1371%2Fjournal.pone.0022207&amp;representation=PDF">PDF</a>.  I'm sure there are others in mainstream NLP journals, but I'm for want of a database that lists them. </p>

CC BY-SA 3.0
2012-02-07T03:34:07.017Z
2012-02-07T03:46:05.763Z
2012-02-07T03:46:05.763Z
Chuck Sherrington
<p>I am looking for scholarly, peer-reviewed articles that discuss natural language processing (NLP) involving chat or text messaging lingo/acronyms and the affect of chat participants based on language and emoticons. Anyone have a good recommendation? </p>

<p>I am already familiar with the <a href="http://arxiv.org/abs/1010.3003">recent work</a> involving using Twitter to assess the general mood of society as a means of predicting the stock market. They used the Google-Profile of Mood States (GPOMS) that measures mood as Calm, Alert, Sure, Vital, Kind, or Happy. I am looking for other examples that assess general affect, ideally by applying more scientific psychometric tests to NLP.</p>

<p>I am not trying to use the data for prediction. I am seeking to use machine learning algorithms to classify/categorize conversations.</p>

<p>References:</p>

<p>Bollen, J., Mao, H., Zeng, X-J. (2011).  Twitter mood predicts the stock market.    <em>Journal of Computational Science, 2</em>(1),1-8.  <a href="http://dx.doi.org/10.1016/j.jocs.2010.12.007">[DOI]</a></p>

CC BY-SA 3.0
2012-10-22T20:53:49.850Z
Just of todays findings: ["Cooooooooooooooollllllllllllll!!!!!!!!!!!!!! Using Word Lengthening to Detect Sentiment in Microblogs"](http://newdesign.aclweb.org/anthology/D/D11/D11-1052.pdf) (I haven't it yet, but its title is so charming that I didn't the urge to post it here).
Piotr Migdal
CC BY-SA 3.0
2016-06-21T19:34:58.383Z
Microsoft Cognitive Sciences has an API for this, although I think it's meant for normal words:  https://text-analytics-demo.azurewebsites.net/
RJB
CC BY-SA 3.0
2016-06-21T19:57:43.387Z
Thanks @RJB - I'll check that out. These days I've also discovered [Lexalytics](https://www.lexalytics.com/)
Dan
<reference-request><emotion><communication><nlp>
How to classify and understand emotions of people using chat and text messaging lingo and emoticons?
Dan
# How to classify and understand emotions of people using chat and text messaging lingo and emoticons?

(Asked by: username_0 on 2012-02-06)

I am looking for scholarly, peer-reviewed articles that discuss natural language processing (NLP) involving chat or text messaging lingo/acronyms and the affect of chat participants based on language and emoticons. Anyone have a good recommendation?

I am already familiar with the [recent work](http://arxiv.org/abs/1010.3003) involving using Twitter to assess the general mood of society as a means of predicting the stock market. They used the Google-Profile of Mood States (GPOMS) that measures mood as Calm, Alert, Sure, Vital, Kind, or Happy. I am looking for other examples that assess general affect, ideally by applying more scientific psychometric tests to NLP.

I am not trying to use the data for prediction. I am seeking to use machine learning algorithms to classify/categorize conversations.

References:

Bollen, J., Mao, H., Zeng, X-J. (2011). Twitter mood predicts the stock market. *Journal of Computational Science, 2*(1),1-8. [[DOI]](http://dx.doi.org/10.1016/j.jocs.2010.12.007)

**Tags:** reference-request, emotion, communication, nlp

> **Comments:**
> **username_1**: Just of todays findings: ["Cooooooooooooooollllllllllllll!!!!!!!!!!!!!! Using Word Lengthening to Detect Sentiment in Microblogs"](http://newdesign.aclweb.org/anthology/D/D11/D11-1052.pdf) (I haven't it yet, but its title is so charming that I didn't the urge to post it here).  
> **username_2**: Microsoft Cognitive Sciences has an API for this, although I think it's meant for normal words:  https://text-analytics-demo.azurewebsites.net/  
> **username_0**: Thanks @username_2 - I'll check that out. These days I've also discovered [Lexalytics](https://www.lexalytics.com/)  

---

## 2 answers

### Answer by username_4 on 2012-02-07. Score: 14 | Accepted answer

The field that is doing this work you describe is [sentiment analysis](http://en.wikipedia.org/wiki/Sentiment_analysis). From Wikipedia:

> A basic task in sentiment analysis is classifying the polarity of a given text at the document, sentence, or feature/aspect level — whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral. Advanced, "beyond polarity" sentiment classification looks, for instance, at emotional states such as "angry," "sad," and "happy."

From [an introductory course](http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html) (lots of good references on this page)

> This work is in the general area of sentiment analysis, opinion extraction or opinion mining, and feature-based opinion summarization from the user-generated content or user-generated media on the Web, e.g., reviews, forum and group discussions, and blogs. In our [KDD-2004](http://www.cs.uic.edu/~liub/publications/kdd04-revSummary.pdf) (Minqing Hu and Bing Liu. "Mining and summarizing customer reviews." Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Seattle, Washington, USA, Aug 22-25, 2004.) paper, we proposed the Feature-Based Opinion Mining model, which is now also called Aspect-Based Opinion Mining (as the term feature here can confuse with the term feature used in machine learning). The output of such opinion mining is a feature-based opinion summary or aspect-based opinion summary. The area is also related to sentiment classification. Our current work is in two main areas, which reflect two kinds of opinions (or evaluations)
>
> * Mining regular (or direct) opinions. Ex: (1). This camera is great. (2). After taking the drug, I got stomach pain.
> * Mining comparative opinions. Ex: Coke tastes better than Pepsi.

In terms of your actual goals, scroll down to [extra credit](http://nlp-s11.utcompling.com/assignments/sentiment-analysis) on this page, and you'll find an exercise that goes through a scenario close to what you describe with emoticons.

In terms of peer-reviewed, Chmiel A, Sienkiewicz J, et al (2011). Collective emotions online and their influence on community life, PLoS One, 6(7), e22207. [PDF](http://www.plosone.org/article/fetchObjectAttachment.action;jsessionid=632B1932DB1134A8B5CD898D126C551B?uri=info%3Adoi%2F10.1371%2Fjournal.pone.0022207&representation=PDF). I'm sure there are others in mainstream NLP journals, but I'm for want of a database that lists them.


---

### Answer by username_3 on 2012-03-28. Score: 7

I would search under the topic of [affective computing](http://en.wikipedia.org/wiki/Affective_computing) especially in *detecting and recognizing emotional information* and then specialize on *NLP* methods in this area.

Here are a few articles of interest:

[Visualizing the affective structure of a text document](http://larifari.org/_/writing/CHI2003-PoseidonsEye.pdf)

[A model of textual affect sensing using real-world knowledge](http://larifari.org/_/writing/IUI2003-AffectSensing.pdf)

[Saurus: an emotionally-weighted thesaurus](http://larifari.org/_/writing/AAAICompAesthetics2006-Saurus.pdf)

---

References:

Gouldstone, J., Liu, H., Lieberman, H., Ishii, H. (2006). Saurus: an emotionally-weighted thesaurus. *Computational aesthetics: Artificial intelligence approaches to beauty and happiness*, Technical Report WS-06-04.

Liu, H., Selker, T., Lieberman, H. (2003). Visualizing the Affective Structure of a Text Document. *Proceedings of the Conference on Human Factors in Computing Systems*, CHI 2003, April 5-10, 2003, Ft. Lauderdale, FL, USA. ACM 2003, ISBN 1-58113-637-4, pp. 740-741.

Liu, H., Lieberman, H., Selker, T. (2003). A Model of Textual Affect Sensing using Real-World Knowledge. *Proceedings of the 2003 International Conference on Intelligent User Interfaces*, IUI 2003, January 12-15, 2003, Miami, FL, USA. ACM 2003, ISBN 1-58113-586-6, pp. 125-132. Miami, Florida.


---

