Improving Attitude Words Classification for Opinion Mining Using Word Embedding
| Autor: | Reynier Ortega-Bueno, Paolo Rosso, José E. Medina-Pagola, Carlos Enrique Muñiz-Cuza |
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| Rok vydání: | 2019 |
| Předmět: |
Word embedding
Spanish language Appraisal framework Computer science 02 engineering and technology Neural network word embedding computer.software_genre 01 natural sciences Opinion mining Attitude classification 0202 electrical engineering electronic engineering information engineering Artificial neural network Orientation (computer vision) business.industry 010401 analytical chemistry Sentiment analysis Appraisal theory Class (biology) 0104 chemical sciences 020201 artificial intelligence & image processing Affect (linguistics) Artificial intelligence business LENGUAJES Y SISTEMAS INFORMATICOS computer Natural language processing |
| Zdroj: | Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications ISBN: 9783030134686 CIARP RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia instname |
| DOI: | 10.1007/978-3-030-13469-3_112 |
| Popis: | [EN] Recognizing and classifying evaluative expressions is an important issue of sentiment analysis. This paper presents a corpus-based method for classifying attitude types (Affect, Judgment and Appreciation) and attitude orientation (positive and negative) of words in Spanish relying on the Attitude system of the Appraisal Theory. The main contribution lies in exploring large and unlabeled corpora using neural network word embedding techniques in order to obtain semantic information among words of the same attitude and orientation class. Experimental results show that the proposed method achieves a good effectiveness and outperforms the state of the art for automatic classification of attitude words in Spanish language. The work of the fourth author was partially supported by the SomEMBED TIN2015-71147-C2-1-P research project (MINECO/FEDER). |
| Databáze: | OpenAIRE |
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