@techreport{TD:100141,
	att_abstract={{Prompt and knowledgeable responses to customers’ emails are critical in maximizing customer satisfaction.
Such emails, often contain complaints about unfair treatment due to negligence, incompetence, rigid protocols,
unfriendly systems, and unresponsive personnel. In this paper, we refer to these emails as emotional emails. They
provide valuable feedback to improve contact center processes and customer care, as well as, to enhance customer
retention. This paper describes a method for extracting salient features and identifying emotional emails in customer
care. Salient features reflect customer frustration, dissatisfaction with the business, and threats to either leave,
take legal action and/or report to authorities. Compared to a baseline system using word unigrams, our proposed
approach with salient features resulted in absolute F-measure improvement of greater then 20%.}},
	att_authors={ng2836, mg1528, gd1676},
	att_categories={C_IIS.11, C_IIS.2},
	att_copyright={{Wiley-Blackwell}},
	att_copyright_notice={{"The definitive version is available at onlinelibrary.wiley.com." {{, 2012-10-04}}{{, http://onlinelibrary.wiley.com/doi/10.1111/j.1467-8640.2012.00454.x/full}}}},
	att_donotupload={},
	att_private={false},
	att_projects={OpinionMining},
	att_tags={Boosting,  Customer Service,  Emotional Emails,  Salient Features,  Support Vector Machines,  Text Classification.},
	att_techdoc={true},
	att_techdoc_key={TD:100141},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:100141_DS1_2011-01-03T21:59:30.637Z.pdf},
	author={Narendra Gupta and Mazin Gilbert and Giuseppe Di fabbrizio},
	institution={{Computational Intelligence, An international Journal}},
	month={October},
	title={{EMOTION DETECTION IN EMAIL CUSTOMER CARE}},
	year=2012,
}