Simple Approaches of Sentiment Analysis via Ensemble Learning

Chalothorn, Tawunrat and Ellman, Jeremy (2015) Simple Approaches of Sentiment Analysis via Ensemble Learning. In: Information Science and Applications. Lecture Notes in Electrical Engineering, 339 (VI). Springer, London, pp. 631-639. ISBN 9783662465776

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Official URL: http://dx.doi.org/10.1007/978-3-662-46578-3_74

Abstract

Twitter has become a popular microblogging tool where users are increasing every minute. It allows its users to post messages of up to 140 characters each time; known as ‘Tweets’. Tweets have become extremely attractive to the marketing sector, since the user can either indicate customer success or presage public relations disasters far more quickly than web pages or traditional media. Moreover, the content of Tweets has become a current active research topic on sentiment polarity as positive or negative. Our experiment of sentiment analysis of contexts of tweets show that the accuracy performance can improve and be better achieved using ensemble learning, which is formed by the majority voting of the Support Vector Machine, Naive Bayes, SentiStrength and Stacking.

Item Type: Book Section
Uncontrolled Keywords: twitter, tweet, natural language processing
Subjects: G400 Computer Science
G700 Artificial Intelligence
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Related URLs:
Depositing User: Ay Okpokam
Date Deposited: 06 Mar 2015 16:58
Last Modified: 20 Dec 2016 15:06
URI: http://nrl.northumbria.ac.uk/id/eprint/21559

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