A comparative study of uncertainty based active learning strategies for general purpose twitter sentiment analysis with deep neural networks
Autor(en): | Haldenwang, N. Ihler, K. Kniephoff, J. Vornberger, O. |
Herausgeber: | Rehm, G. Declerck, T. |
Stichwörter: | Artificial intelligence; Classification (of information); Data mining; Deep learning; Social networking (online); Uncertainty analysis, Active Learning; Active learning strategies; Comparative studies; Equal sizes; Large corpora; Sentiment analysis; Training data; Unlabeled samples, Deep neural networks | Erscheinungsdatum: | 2018 | Herausgeber: | Springer Verlag | Journal: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Volumen: | 10713 LNAI | Startseite: | 208 | Seitenende: | 215 | Zusammenfassung: | Active learning is a common approach when it comes to classification problems where a lot of unlabeled samples are available but the cost of manually annotating samples is high. This paper describes a study of the feasibility of uncertainty based active learning for general purpose Twitter sentiment analysis with deep neural networks. Results indicate that the approach based on active learning is able to achieve similar results to very large corpora of randomly selected samples. The method outperforms randomly selected training data when the amount of training data used for both approaches is of equal size. © 2018, The Author(s). |
Beschreibung: | Conference of 27th International Conference on German Society for Computational Linguistics and Language Technology, GSCL 2017 ; Conference Date: 13 September 2017 Through 14 September 2017; Conference Code:209449 |
ISBN: | 9783319737058 | ISSN: | 03029743 | DOI: | 10.1007/978-3-319-73706-5_18 | Externe URL: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041135410&doi=10.1007%2f978-3-319-73706-5_18&partnerID=40&md5=323928f9120a0a27fceb9135baceed36 |
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geprüft am 02.05.2024