Editing Text mining (section)

=== Sentiment analysis ===
[[Sentiment analysis]] may involve analysis of products such as movies, books, or hotel reviews for estimating how favorable a review is for the product.<ref>{{cite book |doi=10.3115/1118693.1118704 |title=Proceedings of the ACL-02 conference on Empirical methods in natural language processing |year=2002 |last1=Pang |first1=Bo |last2=Lee |first2=Lillian |last3=Vaithyanathan |first3=Shivakumar |volume=10 |pages=79–86|chapter=Thumbs up? |s2cid=7105713 }}</ref>
Such an analysis may need a labeled data set or labeling of the [[affect (psychology)|affectivity]] of words.
Resources for affectivity of words and concepts have been made for [[WordNet]]<ref>{{cite journal
 |author1=Alessandro Valitutti |author2=Carlo Strapparava |author3=Oliviero Stock | title = Developing Affective Lexical Resources
 | journal = PsychNology Journal
 | year = 2005
 | issue = 1
 | pages = 61–83
 | url = http://www.psychnology.org/File/PSYCHNOLOGY_JOURNAL_2_1_VALITUTTI.pdf
 | volume = 2
}}</ref> and [[ConceptNet]],<ref name="camnet">{{cite conference
 | author = Erik Cambria |author2=Robert Speer |author3=Catherine Havasi |author4=Amir Hussain
 | title = SenticNet: a Publicly Available Semantic Resource for Opinion Mining
 | book-title = Proceedings of AAAI CSK
 | year = 2010
 | pages = 14–18
 | url = http://www.aaai.org/ocs/index.php/FSS/FSS10/paper/download/2216/2617.pdf
}}</ref> respectively.

Text has been used to detect emotions in the related area of affective computing.<ref>{{cite journal |doi=10.1109/T-AFFC.2010.1 |title=Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications |year=2010 |last1=Calvo |first1=Rafael A |last2=d'Mello |first2=Sidney |journal=IEEE Transactions on Affective Computing |volume=1 |issue=1 |pages=18–37|s2cid=753606 }}</ref> Text based approaches to affective computing have been used on multiple corpora such as students evaluations, children stories and news stories.