Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-1529
Title: TopicThunder at SemEval-2017 Task 4 : sentiment classification using a convolutional neural network with distant supervision
Authors : Müller, Simon
Huonder, Tobias
Deriu, Jan Milan
Cieliebak, Mark
Proceedings: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Pages : 766
Pages to: 771
Conference details: 11th International Workshop on Semantic Evaluation, Vancouver, Canada, 3-4 August 2017
Publisher / Ed. Institution : Association for Computational Linguistics
Issue Date: 2017
License (according to publishing contract) : Licence according to publishing contract
Type of review: Peer review (Publication)
Language : English
Subjects : Sentiment Analysis; Natural Language Processing
Subject (DDC) : 004: Computer science
005: Computer programming, programs and data
410.285: Computational linguistics
Abstract: In this paper, we propose a classifier for predicting topic-specific sentiments of English Twitter messages. Our method is based on a 2-layer CNN. With a distant supervised phase we leverage a large amount of weakly-labelled training data. Our system was evaluated on the data provided by the SemEval-2017 competition in the Topic-Based Message Polarity Classification subtask, where it ranked 4 th place.
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Publication type: Conference Paper
DOI : 10.21256/zhaw-1529
URI: https://digitalcollection.zhaw.ch/handle/11475/1855
Appears in Collections:Publikationen School of Engineering

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