| Publication type: | Conference paper |
| Type of review: | Not specified |
| 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) |
| Page(s): | 3943 |
| Pages to: | 3947 |
| Conference details: | 9th International Conference on Language Resources and Evaluation, Reykjavik, Iceland, 26-31 May 2014 |
| Issue Date: | 2014 |
| Publisher / Ed. Institution: | Association for Computational Linguistics |
| ISBN: | 978-1-63266-621-5 |
| 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/7360 |
| Fulltext version: | Published version |
| License (according to publishing contract): | Licence according to publishing contract |
| Departement: | School of Engineering |
| Organisational Unit: | Institute of Applied Information Technology (InIT) Institute of Data Analysis and Process Design (IDP) |
| Appears in collections: | Publikationen School of Engineering |
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