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Artículo

A First Approach to Mining Opinions as Multisets through Argumentation

Chesñevar, Carlos IvanIcon ; González, María PaulaIcon ; Grosse, Kathrin; Maguitman, Ana GabrielaIcon
Fecha de publicación: 08/2013
Editorial: Springer
Revista: Lecture Notes In Computer Science
ISSN: 0302-9743
Idioma: Inglés
Tipo de recurso: Artículo publicado
Clasificación temática:
Ciencias de la Computación

Resumen

Web 2.0 technologies have resulted in an exponential growth of text-based opinions coming from different sources (such as online news media, microblogging platforms, social networks, online review systems, etc.). The assessment of such opinions has gained considerable interest within several research communities in Computer Science, particularly in the context of modelling decision making processes. In this context, the scientific study of emotions in opinions associated with a given topic has become particularly relevant. Some approaches for assessing emotions in text-based opinions have been developed, resulting in promising software tools for sentiment analysis. In spite of the existence of such tools, assessing and contrasting text-based opinions is indeed a difficult task. On the one hand, complex opinions are built in many cases bottom up, emerging by aggregation from individual opinions posted online. On the other hand, contradictory and potentially inconsistent information might arise when contrasting such complex opinions. This article introduces an argument-based framework which allows to mine text-based opinions based on incrementally generated topics along with partially-ordered features, which provide a multidimensional comparison criterion. Given a topic, we will model an atomic opinion supporting it as a multiset (or bag) of terms. Atomic opinions can be aggregated, and related to alternative opinions, based on expanded topics. As a result, we will be able to obtain an “opinion analysis tree”, rooted in the first original topic.
Palabras clave: Argumentation , Opinion Mining , Egovernment , Datamining
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info:eu-repo/semantics/restrictedAccess Excepto donde se diga explícitamente, este item se publica bajo la siguiente descripción: Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Unported (CC BY-NC-SA 2.5)
Identificadores
URI: http://hdl.handle.net/11336/12415
URL: http://link.springer.com/chapter/10.1007/978-3-642-39860-5_15
DOI: http://dx.doi.org/10.1007/978-3-642-39860-5_15
Colecciones
Articulos(CCT - BAHIA BLANCA)
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - BAHIA BLANCA
Citación
Chesñevar, Carlos Ivan; González, María Paula; Grosse, Kathrin; Maguitman, Ana Gabriela; A First Approach to Mining Opinions as Multisets through Argumentation; Springer; Lecture Notes In Computer Science; 8068; 8-2013; 195-209
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