Recognition and Linking of Product Mentions in User-generated Contents

• Ano de defesa: 2018
• Autor(a) principal: Vieira, Henry Silva
• Outros Autores: http://lattes.cnpq.br/7635217586028802
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Federal do Amazonas; Instituto de Computação; Brasil; UFAM; Programa de Pós-graduação em Informática
• Palavras-chave em Português: Opinion mining; Extracting target entities; Categorizing target entities; Product recognition; Product linking; CIÊNCIAS EXATAS E DA TERRA: CIÊNCIA DA COMPUTAÇÃO
• Link de acesso: https://tede.ufam.edu.br/handle/tede/6686

Online social media has grown into an essential part of our daily life. Through these media, users exchange information that they generate by using many different communication mechanisms. In this context, more and more users pass on and trust information published by other users on a large variety of topics, including opinion and information about products. Automatically extracting and processing user-generated information in social media can provide relevant information and knowledge to a variety of interesting applications. In particular, one of the content analysis techniques most often applied to social media is that of opinion mining. One of the basic tasks associated with opinion mining is extracting and categorizing target entities, i.e., identifying entity mentions in text, and linking these entity mentions to unique real world entities about which the opinions are made. In our work, we focus on target entities of a specific, and currently relevant, type: consumer electronic products. Such products are the main subject of opinions posted by users on a number of posts in discussion forums and retail sites over the Web. In this work, we are interested in using the unstructured textual content generated by social media users to continuously allow enriching the knowledge about products represented in product catalogs. Therefore, the task we address here is how to recognize and link mentions to products in user generated textual content to the product, from a catalog, they refer to. We claim that two basic sub-tasks arise: first, extraction of target entities mentions from unstructured textual content; second, disambiguation of extracted entities, i.e., linking extracted mentions to their real world counterpart. In this work, we developed methods to address these two sub-tasks. This thesis details these tasks, discusses our ideas for the methods we developed, and presents our contributions and results towards this goal.