NDLI: A business intelligent technique for sentiment estimation by management sectors

Content Provider	IEEE Xplore Digital Library
Author	Rady, S.
Copyright Year	2015
Description	Author affiliation: Inf. Syst. Dept., Ain Shams Univ., Cairo, Egypt (Rady, S.)
Abstract	People express emotions in response to everyday situation and personal communication. With diversity of language expressions, it is challenging to provide an accurate estimation of emotion or sentiment. This paper proposes intelligent technique and system for sentiment estimation and prediction in the business domain. It is useful for management sectors where tools can automatically analyze collected data and reveal employees' opinion about their organization, or any ongoing topic. The challenge in this work is to detect sentiment classes from relatively long text, where writers merge sentences and expressions when asked to write reviews, instead of being directly asked to write their sentiment degree. The approach is data-driven, which uses machine learning to train classifier features to recognize the sentiment. A system is implemented and tested (on real data collected from employee reviews at big IT organizations) towards two and five classification degrees problems. Recorded results prove efficiency of the technique.
Starting Page	370
Ending Page	376
File Size	977782
Page Count	7
File Format	PDF
ISBN	9781509019496
e-ISBN	9781509019502
DOI	10.1109/IntelCIS.2015.7397247
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-12
Publisher Place	Egypt
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Emotion recognition Text analytics Sentiment/emotion analysis Heuristic algorithms Bayesian classification Blogs Companies Machine learning Business intelligence Testing
Content Type	Text
Resource Type	Article

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