Applications of Feature Engineering Techniques for Text Data

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Author(s): Shashwati Mishra (B.J.B. College (Autonomous), Bhubaneswar, India)and Mrutyunjaya Panda (Utkal University, Vani Vihar, Bhubaneswar, India)
Copyright: 2021
Pages: 13
Source title: Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science
Source Author(s)/Editor(s): Mrutyunjaya Panda (Utkal University, India)and Harekrishna Misra (Institute of Rural Management, Anand, India)
DOI: 10.4018/978-1-7998-6659-6.ch010

Keywords: Data Mining and Databases / Engineering Science Reference / Knowledge Management and Library Science / Machine Learning

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Abstract

Feature plays a very important role in the analysis and prediction of data as it carries the most valuable information about the data. This data may be in a structured format or in an unstructured format. Feature engineering process is used to extract features from these data. Selection of features is one of the crucial steps in the feature engineering process. This feature selection process can adopt four different approaches. On that basis, it can be classified into four basic categories, namely filter method, wrapper method, embedded method, and hybrid method. This chapter discusses about different techniques coming under these four categories along with the research work on feature selection.

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Applications of Feature Engineering Techniques for Text Data

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