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Adapting sentiment lexicons using contextual semantics for sentiment analysis of Twitter

Saif, Hassan; He, Yulan; Fernández, Miriam and Alani, Harith (2014). Adapting sentiment lexicons using contextual semantics for sentiment analysis of Twitter. In: The Semantic Web: ESWC 2014 Satellite Events, Springer International Publishing, pp. 54–63.

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DOI (Digital Object Identifier) Link: https://doi.org/10.1007/978-3-319-11955-7_5
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Abstract

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.

Item Type: Conference or Workshop Item
Copyright Holders: 2014 Springer International Publishing Switzerland
ISBN: 3-319-11954-0, 978-3-319-11954-0
ISSN: 0302-9743
Project Funding Details:
Funded Project NameProject IDFunding Body
SENSE4US611242EU-FP7
Extra Information: ESWC 2014 Satellite Events,
Anissaras, Crete, Greece,
May 25-29, 2014,
Revised Selected Papers
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Related URLs:
Item ID: 41401
Depositing User: Hassan Saif
Date Deposited: 26 Nov 2014 10:52
Last Modified: 20 Dec 2017 16:53
URI: http://oro.open.ac.uk/id/eprint/41401
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