Polarity shifting in opinion mining through quantification in English
[EN] Polarity shifting can be considered one of the most challenging problems in the context of sentiment analysis. Polarity shifters are treated as linguistic contextual items that can incre-ment, reduce or neutralise the polarity of a word called `focus¿ included in an opinion. The automatic detec...
| Autores: | , |
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| Formato: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Recursos: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | español |
| OAI Identifier: | oai:riunet.upv.es:10251/213089 |
| Acesso em linha: | https://riunet.upv.es/handle/10251/213089 |
| Access Level: | acceso abierto |
| Palavra-chave: | Minería de opiniones Análisis del sentimiento Cambio de polaridad Intensificación Cuantificación Opinion mining Sentiment analysis Polarity shifting Intensification Quantification FILOLOGIA INGLESA |
| Resumo: | [EN] Polarity shifting can be considered one of the most challenging problems in the context of sentiment analysis. Polarity shifters are treated as linguistic contextual items that can incre-ment, reduce or neutralise the polarity of a word called `focus¿ included in an opinion. The automatic detection of such items enhances performance and accuracy of computational systems for opinion mining. From a symbolic approach, we aim to advance in the automatic processing of the polarity shifters that affect the opinions expressed on tweets in English. To this end, we describe a novel knowledge-based model to deal with quantification in English, which increments or reduces the polarity of opinions. In particular, we explain the linguistic rules of each category of quantification shifter, including information about the scope and direction with respect to the focus. Furthermore, we present the mathematical formulae that calculate the strength of the effect on the prior polarity. Finally, we describe the matrices associated to the linguistic rules, which serve to model the knowledge in text-mining systems |
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