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Title: JOINT_FORCES : unite competing sentiment classifiers with random forest
Authors : Dürr, Oliver
Uzdilli, Fatih
Cieliebak, Mark
Proceedings: Proceedings of the International Workshop on Semantic Evaluation (SemEval-2014)
Pages : 366
Pages to: 369
Conference details: International Workshop on Semantic Evaluation (SemEval-2014), Dublin, August 23-24 2014
Publisher / Ed. Institution : Association for Computational Linguistics
Issue Date: 2014
License (according to publishing contract) : CC BY 4.0: Namensnennung 4.0 International
Type of review: Not specified
Language : English
Subjects : Sentiment analysis; Random forest; Competition; Ensemble method
Subject (DDC) : 410.285: Computational linguistics
Abstract: In this paper, we describe how we created a meta-classifier to detect the message-level sentiment of tweets. We participated in SemEval-2014 Task 9B by combining the results of several existing classifiers using a random forest. The results of 5 other teams from the competition as well as from 7 generalpurpose commercial classifiers were used to train the algorithm. This way, we were able to get a boost of up to 3.24 F1 score points.
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Institute of Data Analysis and Process Design (IDP)
Publication type: Conference Paper
DOI : 10.21256/zhaw-3779
ISBN: 978-1-941643-24-2
Appears in Collections:Publikationen School of Engineering

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