[Corpora-List] Summary of semantic resources for sentiment/polarity

[Roy Bar-Haim]

Below is a summary of the responses I received, grouped into categories.
I would like to thank all the people who replied to my message (their 
names appear in square brackets).

Roy.

**** SURVEY

[Maite Taboada,	Laura Canedo,John Sowa]
Opinion mining and sentiment analysis, by Bo Pang and Lillian Lee
http://www.cs.cornell.edu/home/llee/opinion-mining-sentiment-analysis-survey.html

**** DATASETS / CORPORA

[Maite Taboada]
Bo Pang and Lillian Lee -- Movie Review Data
http://www.cs.cornell.edu/People/pabo/movie-review-data/

Maite Taboada, The SFU Review Corpus
http://www.sfu.ca/~mtaboada/research/SFU_Review_Corpus.html
http://www.sfu.ca/~mtaboada/research/nserc-project.html

[Jonathon Read]
I have a collection of 38 book reviews double-annotated according to
Martin and White's Appraisal, a systemic-functional linguistic theory
of evaluation in English.  The annotations include multi-word
expressions of sentiment annotated with polarity.  Further details are
available in my thesis ( available at 
http://personal.cityu.edu.hk/~jlread/papers/read-us09.pdf)

[Yuval Marton]
Ikeda et al. ("Learning to Shift the Polarity of Words for Sentiment
Classification") use customer reviews and movie reviews:
http://www.cs.uic.edu/~liub/FBS/FBS.html
http://www.cs.cornell.edu/people/pabo/movie-review-data/

Titov and McDonald ("A Joint Model of Text and Aspect Ratings for 
Sentiment Summarization") used reviews from TripAdvisor.com. They might 
make the
resource available.

Turney ("Thumbs Up or Thumbs Down? Semantic Orientation Applied to
Unsupervised Classification of Reviews") collected opinions from
Epinions.com

**** LEXICONS

[Daoud Clarke]
The results of the spin model analysis of Takamura et al. is available on 
the web:
http://www.lr.pi.titech.ac.jp/~takamura/pndic_en.html

[Maite Taboada]
SentiWordNet
http://sentiwordnet.isti.cnr.it/

**** OTHER
[Nicholas Groom]
If you are interested in political analysis, you could have a look at the 
UK-based site Tweetminster (
http://tweetminster.co.uk), which claims that its sentiment engine 
(http://search.tweetminster.co.uk/) 'is now able to accurately 
"understand" the sentiment of 72% of the tweets we analyse'. No tech 
details on the site, though.

[Maite Taboada]
There is a Yahoo group called SentimentAI, with links to other resources:
http://tech.groups.yahoo.com/group/SentimentAI/

The work of Janyce Wiebe:
http://www.cs.pitt.edu/~wiebe/



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