Feature selection for text classification: A review

• Published in: Multimedia tools and applications Vol. 78; no. 3; pp. 3797 - 3816
• Main Authors: Deng, Xuelian, Li, Yuqing, Weng, Jian, Zhang, Jilian
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.02.2019; Springer Nature B.V
• Subjects: Audio data; Bayesian analysis; Classification; Computer Communication Networks; Computer Science; Data processing; Data Structures and Information Theory; Digital media; Feature selection; Image classification; Location based services; Multimedia; Multimedia Information Systems; Neural networks; Recommender systems; Special Purpose and Application-Based Systems; Support vector machines; Text categorization; Text editing; Video data; Feature Selection; Multimedia; Text classifiers; Text classification
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-018-6083-5

Big multimedia data is heterogeneous in essence, that is, the data may be a mixture of video, audio, text, and images. This is due to the prevalence of novel applications in recent years, such as social media, video sharing, and location based services (LBS), etc. In many multimedia applications, for example, video/image tagging and multimedia recommendation, text classification techniques have been used extensively to facilitate multimedia data processing. In this paper, we give a comprehensive review on feature selection techniques for text classification. We begin by introducing some popular representation schemes for documents, and similarity measures used in text classification. Then, we review the most popular text classifiers, including Nearest Neighbor (NN) method, Naïve Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), and Neural Networks. Next, we survey four feature selection models, namely the filter, wrapper, embedded and hybrid, discussing pros and cons of the state-of-the-art feature selection approaches. Finally, we conclude the paper and give a brief introduction to some interesting feature selection work that does not belong to the four models.