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.
WOS:000390993204078
2016
978-1-4799-6664-6
New York
7
IEEE International Conference on Communications
REVIEWED
Event name | Event place | Event date |
Kuala Lumpur, MALAYSIA | MAY 22-27, 2016 | |