Semantic sentiment analysis of Twitter

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Abstract

Sentiment analysis over Twitter offer organisations a fast and effective way to monitor the publics' feelings towards their brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers for Twitter datasets have been researched in recent years with varying results. In this paper, we introduce a novel approach of adding semantics as additional features into the training set for sentiment analysis. For each extracted entity (e.g. iPhone) from tweets, we add its semantic concept (e.g. Apple product) as an additional feature, and measure the correlation of the representative concept with negative/positive sentiment. We apply this approach to predict sentiment for three different Twitter datasets. Our results show an average increase of F harmonic accuracy score for identifying both negative and positive sentiment of around 6.5% and 4.8% over the baselines of unigrams and part-of-speech features respectively. We also compare against an approach based on sentiment-bearing topic analysis, and find that semantic features produce better Recall and F score when classifying negative sentiment, and better Precision with lower Recall and F score in positive sentiment classification.

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Publication date2012
Publication titleThe semantic web – ISWC 2012 : 11th international semantic web conference, Boston, MA, USA, November 11-15, 2012, proceedings, part 1
EditorsPhilippe Cudré-Mauroux, Jeff Heflin, Evren Sirin, et al.
Place of PublicationHeildelberg (DE)
PublisherSpringer
Pages508-524
Number of pages17
Volume7649
ISBN (Electronic)978-3-642-35176-1
ISBN (Print)978-3-642-35175-4
Original languageEnglish
Event11th international semantic web conference - Boston, MA, United States

Publication series

NameLecture notes in computer science
PublisherSpringer
Volume7649
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th international semantic web conference
CountryUnited States
CityBoston, MA
Period11/11/1215/11/12

Keywords

  • sentiment analysis, semantic concepts, feature interpolation

DOI

Employable Graduates; Exploitable Research

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