RT info:eu-repo/semantics/article
T1 A novel flexible feature extraction algorithm for Spanish tweet sentiment analysis based on the context of words.
A1 Sánchez Berriel, Isabel
A1 García Díaz, Pilar
A1 Pontiel Martín, Diego
A1 González Ávila, José Luis
K1 Sentiment analysis
K1 Context semantics
K1 Grouping Genetic Algorithm
K1 Flexible feature extraction
K1 Twitter
AB A tweet polarity classifier is presented with four categories: positive, neutral, negative and no opinion. A grouping genetic algorithm performs feature extraction on the reviews. The feature definition is based on entropy and semantic context described as the relative positions between words. The feature selection is flexible because it is customized to each word studied in the reviews. The algorithm has been applied with two corpuses written in Spanish, of 3,413 tweets and more than 63,000 tweets, to classify an evaluation set of 1,899 reviews. The results were evaluated by the metrics M F1 and accuracy. The algorithm has improved the results of both metrics and on both corpuses compared to the previous literature works, achieving a M F1 of 0.640 and an accuracy of 0.689. The flexibility property in feature extraction has been the major qualitative improvement of the classifier.
YR 2022
FD 2022
LK http://riull.ull.es/xmlui/handle/915/36225
UL http://riull.ull.es/xmlui/handle/915/36225
LA en
NO Artículo revista: Pilar, G. D., Isabel, S. B., Diego, P. M., & Luis, G. A. J. (2023). A novel flexible feature extraction algorithm for Spanish tweet sentiment analysis based on the context of words. Expert Systems with Applications, 212, 118817.DOI https://doi.org/10.1016/J.ESWA.2022.118817
DS Repositorio institucional de la Universidad de La Laguna
RD 10-may-2024