Analyzing Sentiment in a Large Set of Web Data While Accounting for Negation

BMWT Heerschop, P van Iterson, Alexander Hogenboom, Flavius Frasincar, U Kaymak

Research output: Chapter/Conference proceedingChapterAcademic

19 Citations (Scopus)

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
EditorsE. Mugellini, P.S. Szczepaniak, M.C Pettenati, M. Sokhn
Place of PublicationFribourg, Switzerland
PublisherSpringer-Verlag
Pages195-205
Number of pages234
ISBN (Print)9783642180286
DOIs
Publication statusPublished - 2011

Publication series

SeriesAdvances in Intelligent and Soft Computing
Volume86

Research programs

  • EUR ESE 32

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