Abstract
Author Profiling is used to predict the demographic profiles like gender, age, location, native language, and educational background of the authors by analyzing their writing styles. The researchers in Author Profiling proposed various set of stylistic features such as character-based, word-based, content-specific, topic-specific, structural, syntactic, and readability features to differentiate the writing styles of the authors. Feature selection is an important step in the Author Profiling approaches to increase the accuracy of profiles of the authors. Feature selection finds the most relevant features for describing the dataset better than the original set of features. This is achieved by removing redundant and irrelevant features according to important criteria of features using feature selection algorithms. In this work, we experimented with a ReliefF feature selection algorithm to identify the important features in the feature set. The experimentation carried on reviews domain for predicting gender by using various combinations of stylistic features. The experimental results show that the set of features identified by the ReliefF feature selection algorithm obtained good accuracy for gender prediction than the original set of features.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Raghunadha Reddy, T., VishnuVardhan, B., Vijaypal Reddy, P.: A survey on authorship profiling techniques. Int. J. Appl. Eng. Res. 11(5), 3092–3102 (2016)
Sapkota, U., Solorio, T. Montes-y-Gómez, M., RamÃrez-de-la-Rosa, G.: Author profiling for english and spanish text. In: Proceedings of CLEF 2013 Evaluation Labs (2013)
Flekova, L., Gurevych, I.: Can we hide in the web? large scale simultaneous age and gender author profiling in social media. In: Proceedings of CLEF 2013 Evaluation Labs (2013)
Kiprov, Y., Hardalov, M., Nakov, P., Koychev, I.: SU@PAN’2015: experiments in author profiling. In: Proceedings of CLEF 2015 Evaluation Labs (2015)
Meina, M., Brodzi´nska, K., Celmer, B., Czoków, M., Patera, M., Pezacki, J., Wilk, M.: Ensemble-based classification for Author Profiling using various features. In: Proceedings of CLEF 2013 Evaluation Labs (2013)
Rosario Dr, S.F., Thangadurai, K.: RELIEF: feature selection approach. In: Int. J. Innov. Res. Dev. 4(11), 218–224 (2015)
Kononenko, I.: Estimating attributes: analysis and extensions of RELIEF. In: Proceedings of the European Conference on Machine Learning (ECML’94), pp. 171–182 (1994)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Raghunadha Reddy, T., Vishnu Vardhan, B., GopiChand, M., Karunakar, K. (2018). Gender Prediction in Author Profiling Using ReliefF Feature Selection Algorithm. In: Bhateja, V., Coello Coello, C., Satapathy, S., Pattnaik, P. (eds) Intelligent Engineering Informatics. Advances in Intelligent Systems and Computing, vol 695. Springer, Singapore. https://doi.org/10.1007/978-981-10-7566-7_18
Download citation
DOI: https://doi.org/10.1007/978-981-10-7566-7_18
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-7565-0
Online ISBN: 978-981-10-7566-7
eBook Packages: EngineeringEngineering (R0)