Sentiment classification of tweets using hierarchical classification

A. I. Baqapuri, Saad Saleh, M. U. Ilyas, M. M. Khan, A. M. Qamar

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademic

2 Citations (Scopus)
78 Downloads (Pure)


This paper addresses the problem of sentiment classification of short messages on microblogging platforms. We apply machine learning and pattern recognition techniques to design and implement a classification system for microblog messages assigning them into one of three classes: positive, negative or neutral. As part of this work, we contributed a dataset consisting of approximately 10, 000 tweets, each labeled on a five point sentiment scale by three different people. Experiments demonstrate a detection rate between approximately 70% and an average false alarm rate of approximately 18% across all three classes. The developed classifier has been made available for online use.
Original languageEnglish
Title of host publication 2016 IEEE International Conference on Communications (ICC)
Number of pages7
ISBN (Print)978-1-4799-6664-6
Publication statusPublished - 27-May-2016
Externally publishedYes
Event2016 IEEE International Conference on Communications (ICC) - Kuala Lumpur, Malaysia
Duration: 22-May-201627-May-2016

Publication series

NameIEEE International Conference on Communications (ICC)
ISSN (Electronic)1938-1883


Conference2016 IEEE International Conference on Communications (ICC)
CityKuala Lumpur

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