Adapting sentiment lexicons using contextual semantics for sentiment analysis of Twitter


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.

Divisions: College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
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Additional Information: Saif, H; He, Y; Fernández, M; Alani, H 2014 'Adapting sentiment lexicons using contextual semantics for sentiment analysis of Twitter', Proc. SSA-SMILE 2014,
Event Title: 1st workshop on Semantic Sentiment Analysis / workshop on Social Media and Linked Data for Emergency Response / co-located with 11th European Semantic Web Conference (ESWC 2014)
Event Type: Other
Event Dates: 2014-05-25 - 2014-05-25
Uncontrolled Keywords: lexicon adaptation,semantics,sentiment analysis,Twitter,General Computer Science
Last Modified: 27 Jun 2024 12:18
Date Deposited: 01 Jul 2015 14:30
Full Text Link: http://ceur-ws. ... 329/paper_2.pdf
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Conference contribution
Published Date: 2014
Authors: Saif, Hassan
He, Yulan (ORCID Profile 0000-0003-3948-5845)
Fernández, Miriam
Alani, Harith



Version: Published Version

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