Sentiment Analysis using Convolutional Neural Networks with Multi-Task Training and Distant Supervision on Italian Tweets
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28 août 2017
- doi: 10.4000/books.aaccademia.2009
- eisbn: 9788899982553
https://creativecommons.org/licenses/by-nc-nd/4.0/ , info:eu-repo/semantics/openAccess
Mots-clés
linguistica computazionale riconoscimento telefonico articolare annotazione fattualità degli eventi entità chiamata rEcognition e collegamenti nei tweet italiani etichettare per messaggi social media classificazione polarità sentimenti linguistique computationelle reconnaissance téléphonique articulatoire annotation de facturation de l'événement entité appelée rEcognition et liens dans le tweets italien étiqueter les messages des médias sociaux classement polarité sentiments computational linguistics articulatory phone recognition event factuality annotation named entity rEcognition and linking in italian tweets tagging for italian social media texts sentiment polarity classification LAN009000 Linguistics CFSujets proches
PE ratio Earnings multiple Earnings-price ratio Stocks--Price-earnings ratios Pension plans Employee pension trusts Pension funds Price-earnings ratio Ratio C/B Coefficient de capitalisation des résultats CCR Coefficient de capitalisation des bénéfices RCB PER Multiple décapitalisation des résultats Multiple cours-bénéfice Régimes privés de retraite par capitalisation Plans d'épargne en vue de la retraite Plan d'épargne-retraite Comptes d'épargne-retraite Fonds de pension Régimes de retraite par capitalisation PER Retraite-épargneCiter ce document
Jan Deriu et al., « Sentiment Analysis using Convolutional Neural Networks with Multi-Task Training and Distant Supervision on Italian Tweets », Accademia University Press, ID : 10.4000/books.aaccademia.2009
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Résumé
In this paper, we propose a classifier 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.