Twitter as a Comparable Corpus to build ...
Type de document :
Communication dans un congrès avec actes
Titre :
Twitter as a Comparable Corpus to build Multilingual Affective Lexicons
Auteur(s) :
Fraisse, Amel [Auteur]
Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur [LIMSI]
Paroubek, Patrick [Auteur]
Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur [LIMSI]

Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur [LIMSI]
Paroubek, Patrick [Auteur]
Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur [LIMSI]
Titre de la manifestation scientifique :
The 7th Workshop on Building and Using Comparable Corpora
Ville :
Reykjavik
Pays :
Islande
Date de début de la manifestation scientifique :
2014-05-26
Titre de l’ouvrage :
The 7th Workshop on Building and Using Comparable Corpora Proceedings
Mot(s)-clé(s) en anglais :
Affective Lexicon
Comparable Corpus
Sentiment Analysis
Comparable Corpus
Sentiment Analysis
Discipline(s) HAL :
Informatique [cs]/Traitement du texte et du document
Résumé en anglais : [en]
The main issue of any lexicon-based sentiment analysis system is the lack of affective lexicons. Such lexicons contain lists of words annotated with their affective classes. There exist some number of such resources but ...
Lire la suite >The main issue of any lexicon-based sentiment analysis system is the lack of affective lexicons. Such lexicons contain lists of words annotated with their affective classes. There exist some number of such resources but only for few languages and often for a small number of affective classes, generally restricted to two classes (positive and negative). In this paper we propose to use Twitter as a comparable corpus to generate a fine-grained and multilingual affective lexicons. Our approach is based in the co-occurence between English and target affective words in the same emotional corpus. And it can be applied to any number of target languages. In this paper we describe the building of affective lexicons for seven languages (en, fr, de, it, es, pt, ru).Lire moins >
Lire la suite >The main issue of any lexicon-based sentiment analysis system is the lack of affective lexicons. Such lexicons contain lists of words annotated with their affective classes. There exist some number of such resources but only for few languages and often for a small number of affective classes, generally restricted to two classes (positive and negative). In this paper we propose to use Twitter as a comparable corpus to generate a fine-grained and multilingual affective lexicons. Our approach is based in the co-occurence between English and target affective words in the same emotional corpus. And it can be applied to any number of target languages. In this paper we describe the building of affective lexicons for seven languages (en, fr, de, it, es, pt, ru).Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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- BUCC14.pdf
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