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Title: Sentiment analysis using convolutional neural networks with multi-task training and distant supervision on italian tweets
Authors : Deriu, Jan Milan
Cieliebak, Mark
Proceedings: Proceedings of Third Italian Conference on Computational Linguistics (CLiC-it 2016) & Fifth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (EVALITA 2016)
Conference details: Fifth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian, Napoli, Italy, December 5-7, 2016
Editors of the parent work: Basili, Roberto
Montemagni, Simonetta
Publisher / Ed. Institution : Italian Journal of Computational Linguistics
Issue Date: 2016
License (according to publishing contract) : Licence according to publishing contract
Type of review: Peer review (Publication)
Language : English
Subjects : Sentiment Analysis
Subject (DDC) : 004: Computer science
005: Computer programming, programs and data
410.285: Computational linguistics
Abstract: In this paper, we propose a clas-sifier for predicting sentiments of Italian Twitter messages. This work builds upon a deep learning approach where we leverage large amounts of weakly labelled data to train a 2-layer convolutional neural network. To train our network we apply a form of multi-task training. Our system participated in the EvalItalia-2016 competition and outperformed all other approaches on the sentiment analysis task.
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Publication type: Conference Paper
DOI : 10.21256/zhaw-1527
Appears in Collections:Publikationen School of Engineering

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