Sentiment Classification of Tweets using Hierarchical Classification

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.


Published in:
2016 Ieee International Conference On Communications (Icc)
Presented at:
IEEE International Conference on Communications (ICC), Kuala Lumpur, MALAYSIA, MAY 22-27, 2016
Year:
2016
Publisher:
New York, Ieee
ISSN:
1550-3607
ISBN:
978-1-4799-6664-6
Laboratories:




 Record created 2017-02-17, last modified 2019-08-12


Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)