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Vergleich verschiedener Lernmethoden neuronaler Netze bei der Analyse von „Social Media“ Inhalten

  • The goal of this bachelor thesis was the comparison of different learning methods in neural networks. The methods were applied to detect hate posts on social media plat-forms like twitter. To achieve this, a supervised Recurrent Neural Network and a self-supervised Word2Vec model were implemented. The results of both implementations show the importance of choosing the correct dataset and a learning method generating significant results. The problems of both implementations were identified and formulated into possible solutions to achieve more accurate predictions in future. This thesis is of high interest for students and developers in the area of sentiment analysis.

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Author:Alasdair Maclachlan
Referee:Birgit Bertelsmeier, Heiner Klocke
Document Type:Bachelor Thesis
Publishing Institution:Hochschulbibliothek der Technischen Hochschule Köln
Granting Institution:Technische Hochschule Köln
Date of Publication (online):2018/09/07
GND-Keyword:Datenverarbeitung; Informatik; Künstliche Intelligenz
Tag:Neuronale Netze
Page Number:58
Institutes:Informatik und Ingenieurwissenschaften (F10) / Fakultät 10 / Institut für Informatik
CCS-Classification:G. Mathematics of Computing
Dewey Decimal Classification:000 Allgemeines, Informatik, Informationswissenschaft
JEL-Classification:C Mathematical and Quantitative Methods
Open Access:Open Access
Licence (German):License LogoEs gilt das UrhG