Search results for "Tweets"

showing 4 items of 4 documents

ChiLab4It system in the QA4FAQ competition

2017

ChiLab4It is the Question Answering system (QA) for Frequently Asked Questions (FAQ) developed by the Computer-Human Interaction Laboratory (ChiLab) at the University of Palermo for participating to the QA4FAQ task at EVALITA 2016 competition. The system is the versioning of the QuASIt framework developed by the same authors, which has been customized to address the particular task. This technical report describes the strategies that have been imported from QuASIt for implementing ChiLab4It, the actual system implementation, and the comparative evaluations with the results of the other participant tools, as provided by the organizers of the task. ChiLab4It was the only system whose score re…

Computer scienceentité appelée rEcognition et liens dans le tweets italiensentiment polarity classificationevent factuality annotationetichettare per messaggi social mediaclassificazione polarità sentimentitagging for italian social media textsCompetition (economics)computational linguisticsLAN009000linguistica computazionaleSettore ING-INF/05 - Sistemi Di Elaborazione Delle Informazionireconnaissance téléphonique articulatoireQuestion answering FCG Cognitive systemarticulatory phone recognitionLinguisticsCFInternational economicsannotazione fattualità degli eventiriconoscimento telefonico articolarenamed entity rEcognition and linking in italian tweetslinguistique computationelleclassement polarité sentimentsentità chiamata rEcognition e collegamenti nei tweet italianiétiqueter les messages des médias sociauxannotation de facturation de l'événement
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COURAGE at CheckThat! 2022: Harmful Tweet Detection using Graph Neural Networks and ELECTRA

2022

In this paper we propose a deep learning model based on graph machine learning (i.e. Graph Attention Convolution) and a pretrained transformer language model (i.e. ELECTRA). Our model was developed to detect harmful tweets about COVID-19 and was used to tackle subtask 1C (harmful tweet detection) at the CheckThat!Lab shared task organized as part of CLEF 2022. In this binary classification task, our proposed model reaches a binary F1 score (positive class label, i.e. harmful tweet) of 0.28 on the test set. We demonstrate that our approach outperforms the official baseline by 8% and describe our model as well as the experimental setup and results in detail. We also refer to limitations of th…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazionitext graph classificationTwitterCOVID-19harmful tweetELECTRAText graph classification harmful tweets Twitter ELECTRA COVID-19ING-INF/05 - SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI
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Lingvistiskās pazīmes tvītos

2018

Mūsdienās, gan personiskajā, gan profesionālajā dzīvē liela nozīme ir komunikācijas prasmēm. Dažādas sociālās platformas kā, piemēram, Tviters sniedz iespēju komunicēt efektīvak, dalīties ar nozīmīgu informāciju un publiski izteikt savu viedokli. Bakalaura darba mērķis ir izpētīt lingvistiskās iezīmes, kas raksturo tvītus. Kā pētījuma objekts tika izvēlēti politiķa ASV prezidenta Donalda Trampa tvīti. Pētījuma materiāli sastāvēja no 100 nejauši izvēlētiem tvītiem. Tika pielietotas divas pētījuma metodes – diskursa analīze un vārdu biežuma analīze. Diskursa analīzes metode tika pielietota ar mērķi noteikt Donalda Trampa valodas lietojuma paradumus. Savukārt, vārdu biežuma analīzes metode ļāv…

Social mediaTweetsValodniecībaTwitterLinguistic featuresDiscourse analysis
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How Do Explicitly Expressed Emotions Influence Interpersonal Communication and Information Dissemination? : A Field Study of Emoji's Effects on Comme…

2016

emojitkeskinäisviestintätunteetretweetsinformation diffusionmikroblogitcomment
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