Adapting sentiment lexicons using contextual semantics for sentiment analysis of twitter

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Sentiment lexicons for sentiment analysis offer a simple, yet effective way to obtain the prior sentiment information of opinionated words in texts. However, words’ sentiment orientations and strengths often change throughout various contexts in which the words appear. In this paper, we propose a lexicon adaptation approach that uses the contextual semantics of words to capture their contexts in tweet messages and update their prior sentiment orientations and/or strengths accordingly. We evaluate our approach on one state-of-the-art sentiment lexicon using three different Twitter datasets. Results show that the sentiment lexicons adapted by our approach outperform the original lexicon in accuracy and F-measure in two datasets, but give similar accuracy and slightly lower F-measure in one dataset.

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Publication date16 Oct 2014
Publication titleThe semantic web : ESWC 2014 satellite events : ESWC 2014 satellite events, Anissaras, Crete, Greece, May 25-29, 2014, revised selected papers
Place of PublicationChem (CH)
Number of pages10
ISBN (Electronic)978-3-319-11955-7
ISBN (Print)978-3-319-11954-0
Original languageEnglish
Event11th international Conference on Semantic Web: trends and challenges - Anissaras, Crete, Greece
Duration: 25 May 201429 May 2014

Publication series

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


Conference11th international Conference on Semantic Web: trends and challenges
Abbreviated titleESWC 2014
CityAnissaras, Crete


  • lexicon adaptation, semantics, sentiment analysis, Twitter

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