Anzeige
Erschienen in:
2017 | OriginalPaper | Buchkapitel
Firefly Algorithm for Feature Selection in Sentiment Analysis
verfasst von : Akshi Kumar, Renu Khorwal
Erschienen in: Computational Intelligence in Data Mining
Verlag: Springer Singapore
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
Selecting and extracting feature is a vital step in sentiment analysis. The statistical techniques of feature selection like document frequency thresholding produce sub-optimal feature subset because of the non-polynomial (NP)-hard character of the problem. Swarm intelligence algorithms are used extensively in optimization problems. Swarm optimization renders feature subset selection by improving the classification accuracy and reducing the computational complexity and feature set size. In this work, we propose firefly algorithm for feature subset selection optimization. SVM classifier is used for the classification task. Four different datasets are used for the classification of which two are in Hindi and two in English. The proposed method is compared with feature selection using genetic algorithm. This method, therefore, is successful in optimizing the feature set and improving the performance of the system in terms of accuracy.