Título
Classification of attitude words for opinion mining
Autor
LARITZA HERNANDEZ ROJAS
AURELIO LOPEZ LOPEZ
Nivel de Acceso
Acceso Abierto
Materias
Opinion Extraction - (OPINION EXTRACTION) Appraisal Theory - (APPRAISAL THEORY) Corpus Evaluation - (CORPUS EVALUATION) Machine Learning - (MACHINE LEARNING) CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA - (CTI) MATEMÁTICAS - (CTI) CIENCIA DE LOS ORDENADORES - (CTI) CIENCIA DE LOS ORDENADORES - (CTI)
Resumen o descripción
This work details appraisal extraction from attitude expressions. Here, by attitude expressions, we refer to those single words that convey the evaluation of sentiments or emotional states, about human behaviors, objects, processes or people, according to the Appraisal Theory of language. The attitude words can be classified into affect, judgment, and appreciation; either positive or negative. Extraction of the attitude words has a significant range of applications from opinion extraction and summarization, up to temporal opinion analysis. To determine the attitude, we use two machine learning techniques; namely, Support Vector Machines and Random Forest. These algorithms classify a given word starting from a vector that represents the information from the context where the words tend to occur. On the other hand, we can observe the context of the words relying on a corpus of sentences from user generated contents, such as reviews, editorials and other online texts.
Editor
IJCLA
Fecha de publicación
2011
Tipo de publicación
Artículo
Versión de la publicación
Versión aceptada
Recurso de información
Formato
application/pdf
Idioma
Inglés
Audiencia
Estudiantes
Investigadores
Público en general
Sugerencia de citación
Hernadez-Rojas, L., et al., (2011). Classification of attitude words for opinion mining, IJCLA, Vol. 2 (1–2): 267–283
Repositorio Orígen
Repositorio Institucional del INAOE
Descargas
333