Title: Meta-classifiers easily improve commercial sentiment detection tools
Authors : Cieliebak, Mark
Dürr, Oliver
Uzdilli, Fatih
Proceedings: Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC 2014)
Pages : 3943
Pages to: 3947
Conference details: 9th International Conference on Language Resources and Evaluation (LREC 2014), Reykjavik, 26-31 May 2014
Publisher / Ed. Institution : Association for Computational Linguistics
Issue Date: 2014
License (according to publishing contract) : Licence according to publishing contract
Type of review: Not specified
Language : English
Subjects : Opinion mining; Machine learning; Sentiment analysis; Corpus analytics
Subject (DDC) : 410.285: Computational linguistics
Abstract: In this paper, we analyze the quality of several commercial tools for sentiment detection. All tools are tested on nearly 30,000 short texts from various sources, such as tweets, news, reviews etc. The best commercial tools have average accuracy of 60%. We then apply machine learning techniques (Random Forests) to combine all tools, and show that this results in a meta-classifier that improves the overall performance significantly.
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Institute of Data Analysis and Process Design (IDP)
Publication type: Conference Paper
ISBN: 978-1-63266-621-5
URI: https://digitalcollection.zhaw.ch/handle/11475/7360
Appears in Collections:Publikationen School of Engineering

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