Analyzing sentiment in a large set of web data while accounting for negation

B.M.W.T. Heerschop, P. Iterson, van, A.C. Hogenboom, F. Frasincar, U. Kaymak

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

14 Citations (Scopus)
135 Downloads (Pure)

Abstract

As virtual utterances of opinions or sentiment are becoming increasingly abundant on the Web, automated ways of analyzing sentiment in such data are becoming more and more urgent. In this paper, we provide a classification scheme for existing approaches to document sentiment analysis. As the role of negations in sentiment analysis has been explored only to a limited extent, we additionally investigate the impact of taking into account negation when analyzing sentiment. To this end, we utilize a basic sentiment analysis framework – consisting of a wordbank creation part and a document scoring part – taking into account negation. Our experimental results show that by accounting for negation, precision on human ratings increases with 1.17%. On a subset of selected documents containing negated words, precision increases with 2.23%.
Original languageEnglish
Title of host publicationAdvances in Intelligent Web Mastering - 3 (Proceedings of the 7th Atlantic Web Intelligence Conference, AWIC 2011, Fribourg, Switzerland, January 26-28 2011)
EditorsE. Mugellini, P.S. Szczepaniak, M.C. Pettenati, M. Sokhn
Place of PublicationBerlin
PublisherSpringer
Pages195-205
ISBN (Print)978-3-642-18028-6
DOIs
Publication statusPublished - 2011
Eventconference; 7th Atlantic Web Intelligence Conference (AWIC 2011); 2011-01-26; 2011-01-28 -
Duration: 26 Jan 201128 Jan 2011

Publication series

NameAdvances in Intelligent and Soft Computing
Volume86
ISSN (Print)1867-5662

Conference

Conferenceconference; 7th Atlantic Web Intelligence Conference (AWIC 2011); 2011-01-26; 2011-01-28
Period26/01/1128/01/11
Other7th Atlantic Web Intelligence Conference (AWIC 2011)

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