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A Brief Study of Approaches to Text Feature Selection

A Brief Study of Approaches to Text Feature Selection

Ravindra Babu Tallamaraju, Manas Kirti
Copyright: © 2018 |Pages: 28
ISBN13: 9781522528050|ISBN10: 1522528059|EISBN13: 9781522528067
DOI: 10.4018/978-1-5225-2805-0.ch009
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MLA

Tallamaraju, Ravindra Babu, and Manas Kirti. "A Brief Study of Approaches to Text Feature Selection." Modern Technologies for Big Data Classification and Clustering, edited by Hari Seetha, et al., IGI Global, 2018, pp. 216-243. https://doi.org/10.4018/978-1-5225-2805-0.ch009

APA

Tallamaraju, R. B. & Kirti, M. (2018). A Brief Study of Approaches to Text Feature Selection. In H. Seetha, M. Murty, & B. Tripathy (Eds.), Modern Technologies for Big Data Classification and Clustering (pp. 216-243). IGI Global. https://doi.org/10.4018/978-1-5225-2805-0.ch009

Chicago

Tallamaraju, Ravindra Babu, and Manas Kirti. "A Brief Study of Approaches to Text Feature Selection." In Modern Technologies for Big Data Classification and Clustering, edited by Hari Seetha, M. Narasimha Murty, and B. K. Tripathy, 216-243. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-2805-0.ch009

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Abstract

With reducing cost of storage devices, increasing amounts of data is being stored and processed for extracting intelligence. Classification and clustering have been two major approaches in generating data abstraction. Over the last few years, text data is dominating the types of data shared and stored. Some of the sources of such datasets are mobile data, e-commerce, and wide-range of continuously expanding social-networking services. Within each of these sources, the nature of data differs drastically from formal language text to Twitter or SMS slangs thereby leading to the need for different ways of processing the data for making meaningful summarization. Such summaries could effectively be used for business advantage. Processing of such data requires identifying appropriate set of features both for efficiency and effectiveness. In the current Chapter, we propose to discuss approaches to text feature selection and make a comparative study.

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