Recognition and Linking of Product Mentions in User-generated Contents
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...
| Autor principal: | Vieira, Henry Silva |
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| Outros Autores: | http://lattes.cnpq.br/7635217586028802 |
| Grau: | Tese |
| Idioma: | eng |
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Universidade Federal do Amazonas
2018
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https://tede.ufam.edu.br/handle/tede/6686 |
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oai:https:--tede.ufam.edu.br-handle-:tede-66862018-10-17T05:03:26Z Recognition and Linking of Product Mentions in User-generated Contents Vieira, Henry Silva Silva, Altigran Soares da http://lattes.cnpq.br/7635217586028802 http://lattes.cnpq.br/3405503472010994 Moura, Edleno Silva de Calado, Pável Pereira Marinho, Leandro Balby Opinion mining Extracting target entities Categorizing target entities Product recognition Product linking CIÊNCIAS EXATAS E DA TERRA: CIÊNCIA DA COMPUTAÇÃO 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. A mídia social online tornou-se uma parte essencial de nossa vida diária. Por meio dessas mídias, os usuários trocam informações que geram usando diversos mecanismos de comunicação. Nesse contexto, mais e mais usuários transmitem e confiam em informações publicadas por outros usuários sobre uma grande variedade de tópicos, incluindo opiniões e informações sobre produtos. A extração e o processamento automáticos de informações geradas pelo usuário nas mídias sociais podem fornecer informações e conhecimento relevantes para uma variedade de aplicativos interessantes. Em particular, uma das técnicas de análise de conteúdo mais aplicadas às mídias sociais é a de mineração de opinião. Uma das tarefas básicas associadas à mineração de opinião é extrair e categorizar as entidades de destino, ou seja, identificar as menções de entidade no texto e vincular essas menções de entidade a entidades do mundo real sobre as quais as opiniões são feitas. Em nosso trabalho, nos concentramos em entidades-alvo de um tipo específico e atualmente relevante: produtos eletrônicos de consumo. Tais produtos são o principal assunto de opiniões postadas pelos usuários em várias postagens em fóruns de discussão e sites de varejo na Web. Neste trabalho, estamos interessados em usar o conteúdo textual não estruturado gerado por usuários de mídia social para permitir continuamente enriquecer o conhecimento sobre produtos representados em catálogos de produtos. Portanto, a tarefa que abordamos aqui é como reconhecer e vincular menções a produtos em conteúdo textual gerado pelo usuário para o produto, de um catálogo, ao qual eles se referem. Afirmamos que duas sub-tarefas básicas surgem: primeiro, a extração de entidades alvo mencionada em conteúdo textual não-estruturado; segundo, a desambiguação de entidades extraídas, isto é, ligação menções extraídas à sua contraparte do mundo real. Neste trabalho, desenvolvemos métodos para abordar essas duas subtarefas. Esta tese detalha essas tarefas, discute nossas ideias para os métodos que desenvolvemos e apresenta nossas contribuições e resultados para esse objetivo. CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior FAPEAM - Fundação de Amparo à Pesquisa do Estado do Amazonas Não tive dificuldades, tudo funcionou corretamente. 2018-10-16T17:41:31Z 2018-09-25 Tese VIEIRA, Henry Silva. Recognition and Linking of Product Mentions in User-generated Contents. 2018. 127 f. Tese (Doutorado em Informática) - Universidade Federal do Amazonas, Manaus, 2018. https://tede.ufam.edu.br/handle/tede/6686 eng Acesso Aberto http://creativecommons.org/licenses/by/4.0/ application/pdf Universidade Federal do Amazonas Instituto de Computação Brasil UFAM Programa de Pós-graduação em Informática |
| institution |
TEDE - Universidade Federal do Amazonas |
| collection |
TEDE-UFAM |
| language |
eng |
| topic |
Opinion mining Extracting target entities Categorizing target entities Product recognition Product linking CIÊNCIAS EXATAS E DA TERRA: CIÊNCIA DA COMPUTAÇÃO |
| spellingShingle |
Opinion mining Extracting target entities Categorizing target entities Product recognition Product linking CIÊNCIAS EXATAS E DA TERRA: CIÊNCIA DA COMPUTAÇÃO Vieira, Henry Silva Recognition and Linking of Product Mentions in User-generated Contents |
| topic_facet |
Opinion mining Extracting target entities Categorizing target entities Product recognition Product linking CIÊNCIAS EXATAS E DA TERRA: CIÊNCIA DA COMPUTAÇÃO |
| description |
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. |
| author_additional |
Silva, Altigran Soares da |
| author_additionalStr |
Silva, Altigran Soares da |
| format |
Tese |
| author |
Vieira, Henry Silva |
| author2 |
http://lattes.cnpq.br/7635217586028802 |
| author2Str |
http://lattes.cnpq.br/7635217586028802 |
| title |
Recognition and Linking of Product Mentions in User-generated Contents |
| title_short |
Recognition and Linking of Product Mentions in User-generated Contents |
| title_full |
Recognition and Linking of Product Mentions in User-generated Contents |
| title_fullStr |
Recognition and Linking of Product Mentions in User-generated Contents |
| title_full_unstemmed |
Recognition and Linking of Product Mentions in User-generated Contents |
| title_sort |
recognition and linking of product mentions in user-generated contents |
| publisher |
Universidade Federal do Amazonas |
| publishDate |
2018 |
| url |
https://tede.ufam.edu.br/handle/tede/6686 |
| _version_ |
1831969672042381312 |
| score |
11.753735 |