Semantic patterns for sentiment analysis of Twitter

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Abstract

Most existing approaches to Twitter sentiment analysis assume that sentiment is explicitly expressed through affective words. Nevertheless, sentiment is often implicitly expressed via latent semantic relations, patterns and dependencies among words in tweets. In this paper, we propose a novel approach that automatically captures patterns of words of similar contextual semantics and sentiment in tweets. Unlike previous work on sentiment pattern extraction, our proposed approach does not rely on external and fixed sets of syntactical templates/patterns, nor requires deep analyses of the syntactic structure of sentences in tweets. We evaluate our approach with tweet- and entity-level sentiment analysis tasks by using the extracted semantic patterns as classification features in both tasks. We use 9 Twitter datasets in our evaluation and compare the performance of our patterns against 6 state-of-the-art baselines. Results show that our patterns consistently outperform all other baselines on all datasets by 2.19% at the tweet-level and 7.5% at the entity-level in average F-measure.

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Publication date2014
Publication titleThe Semantic Web – ISWC 2014 : 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014, proceedings, part II
EditorsPeter Mika, Tania Tudorache, Abraham Bernstein, Chris Welty, et al
Place of PublicationChem (CH)
PublisherSpringer
Pages324-340
Number of pages17
ISBN (Electronic)978-3-319-11915-1
ISBN (Print)978-3-319-11914-4
Original languageEnglish
Event13th International Semantic Web Conference - Riva del Garda, Italy

Publication series

NameLecture notes in computer science
PublisherSpringer
Volume8797
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Semantic Web Conference
Abbreviated titleISWC 2014
CountryItaly
CityRiva del Garda
Period19/10/1423/10/14

    Keywords

  • semantic patterns, sentiment analysis, Twitter

DOI

Research outputs

Employable Graduates; Exploitable Research

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