Improving opinion retrieval in social media by combining features-based coreferencing and memory-based learning

John Atkinson, Gonzalo Salas, Alejandro Figueroa

Resultado de la investigación: Article

9 Citas (Scopus)

Resumen

Social networks messaging typically contains a lot of implicit linguistic information partially due to restrictions on a message's length (i.e., few named entities, short sentences, no discourse structure, etc.). This may significantly impact several applications including opinion mining, sentiment analysis, etc., as data collection tasks such as opinion retrieval tasks will fail to obtain all the relevant messages whenever the target topic, objects, or features are not explicit within the texts. In order to address these issues, in this paper a novel adaptive approach for opinion retrieval is proposed. It combines natural-language co-referencing techniques, features-based linguistic preprocessing and memory-based learning to resolving implicit co-referencing within informal opinion texts by using underlying hierarchies of thread messages. Experiments were conducted to assess the ability of the model to improve opinion retrieval by resolving implicit entities and features, showing the promise of our opinion retrieval approach when compared to state-of-the-art methods using text data from social networks.

Idioma originalEnglish
Páginas (desde-hasta)20-31
Número de páginas12
PublicaciónInformation Sciences
Volumen299
DOI
EstadoPublished - 1 ene 2015

Huella dactilar

Social Media
Linguistics
Retrieval
Data storage equipment
Social Networks
Opinion Mining
Sentiment Analysis
Thread
Natural Language
Preprocessing
Experiments
Restriction
Target
Learning
Social media
Experiment
Text
Social networks
Model

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Software
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

Citar esto

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abstract = "Social networks messaging typically contains a lot of implicit linguistic information partially due to restrictions on a message's length (i.e., few named entities, short sentences, no discourse structure, etc.). This may significantly impact several applications including opinion mining, sentiment analysis, etc., as data collection tasks such as opinion retrieval tasks will fail to obtain all the relevant messages whenever the target topic, objects, or features are not explicit within the texts. In order to address these issues, in this paper a novel adaptive approach for opinion retrieval is proposed. It combines natural-language co-referencing techniques, features-based linguistic preprocessing and memory-based learning to resolving implicit co-referencing within informal opinion texts by using underlying hierarchies of thread messages. Experiments were conducted to assess the ability of the model to improve opinion retrieval by resolving implicit entities and features, showing the promise of our opinion retrieval approach when compared to state-of-the-art methods using text data from social networks.",
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Improving opinion retrieval in social media by combining features-based coreferencing and memory-based learning. / Atkinson, John; Salas, Gonzalo; Figueroa, Alejandro.

En: Information Sciences, Vol. 299, 01.01.2015, p. 20-31.

Resultado de la investigación: Article

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T1 - Improving opinion retrieval in social media by combining features-based coreferencing and memory-based learning

AU - Atkinson, John

AU - Salas, Gonzalo

AU - Figueroa, Alejandro

PY - 2015/1/1

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KW - Linguistic coreferencing

KW - Memory-based learning

KW - Natural language processing

KW - Opinion mining

KW - Opinion retrieval

KW - Text mining

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