EPJ Data Sci. (2020) 9: 26
https://doi.org/10.1140/epjds/s13688-020-00244-9

Regular article

Enriching feature engineering for short text samples by language time series analysis

¹ Department of Electrical, Computer, and Software Engineering, University of Auckland, 20 Symonds St, 1010, Auckland, New Zealand
² Department of Engineering Science, University of Auckland, 70 Symonds St, 1010, Auckland, New Zealand

^* e-mail: a.kempa-liehr@auckland.ac.nz

Received: 28 November 2019
Accepted: 20 August 2020
Published online: 31 August 2020

Abstract

In this case study, we are extending feature engineering approaches for short text samples by integrating techniques which have been introduced in the context of time series classification and signal processing. The general idea of the presented feature engineering approach is to tokenize the text samples under consideration and map each token to a number, which measures a specific property of the token. Consequently, each text sample becomes a language time series, which is generated from consecutively emitted tokens, and time is represented by the position of the respective token within the text sample. The resulting language time series can be characterised by collections of established time series feature extraction algorithms from time series analysis and signal processing. This approach maps each text sample (irrespective of its original length) to 3970 stylometric features, which can be analysed with standard statistical learning methodologies. The proposed feature engineering technique for short text data is applied to two different corpora: the Federalist Papers data set and the Spooky Books data set. We demonstrate that the extracted language time series features can be successfully combined with standard machine learning approaches for natural language processing and have the potential to improve the classification performance. Furthermore, the suggested feature engineering approach can be used for visualizing differences and commonalities of stylometric features. The presented framework models the systematic feature engineering based on approaches from time series classification and develops a statistical testing methodology for multi-classification problems.