Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-3779| Publication type: | Conference paper |
| Type of review: | Not specified |
| Title: | JOINT_FORCES : unite competing sentiment classifiers with random forest |
| Authors: | Dürr, Oliver Uzdilli, Fatih Cieliebak, Mark |
| DOI: | 10.21256/zhaw-3779 |
| Proceedings: | Proceedings of the International Workshop on Semantic Evaluation (SemEval-2014) |
| Page(s): | 366 |
| Pages to: | 369 |
| Conference details: | International Workshop on Semantic Evaluation (SemEval-2014), Dublin, Irland, 23-24 August 2014 |
| Issue Date: | 2014 |
| Publisher / Ed. Institution: | Association for Computational Linguistics |
| ISBN: | 978-1-941643-24-2 |
| 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 general purpose 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/7363 |
| Fulltext version: | Published version |
| License (according to publishing contract): | CC BY 4.0: Attribution 4.0 International |
| 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 |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| SemEval062-1.pdf | 428.43 kB | Adobe PDF |  View/Open |
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