Gender Classification of Web Authors Using Feature Selection and Language Models
(2015) In Lecture Notes in Computer Science 9319. p.226-233- Abstract
- In the present article, we address the problem of automatic gender classification of web blog authors. More specifically, we employ eight widely used machine learning algorithms, in order to study the effectiveness of feature selection on improving the accuracy of gender classification. The feature ranking is performed over a set of statistical, part-of-speech tagging and language model features. In the experiments, we employed classification models based on decision trees, support vector machines and lazy-learning algorithms. The experimental evaluation performed on blog author gender classification data demonstrated the importance of language model features for this task and that feature selection significantly improves the accuracy of... (More)
- In the present article, we address the problem of automatic gender classification of web blog authors. More specifically, we employ eight widely used machine learning algorithms, in order to study the effectiveness of feature selection on improving the accuracy of gender classification. The feature ranking is performed over a set of statistical, part-of-speech tagging and language model features. In the experiments, we employed classification models based on decision trees, support vector machines and lazy-learning algorithms. The experimental evaluation performed on blog author gender classification data demonstrated the importance of language model features for this task and that feature selection significantly improves the accuracy of gender classification, regardless of the type of the machine learning algorithm used. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/e4a3881a-0566-4dfa-a411-19ff9c0f0102
- author
- Aravantinou, Christina ; Simaki, Vasiliki LU ; Mporas, Iosif and Megalooikonomou, Vasileios
- publishing date
- 2015
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Text classification, Gender identification, Feature selection
- host publication
- Speech and Computer : 17th International Conference, SPECOM 2015, Athens, Greece, September 20-24, 2015, Proceedings - 17th International Conference, SPECOM 2015, Athens, Greece, September 20-24, 2015, Proceedings
- series title
- Lecture Notes in Computer Science
- editor
- Ronzhin, Andrey ; Potapova, Rodmonga and Fakotakis, Nikos
- volume
- 9319
- pages
- 226 - 233
- publisher
- Springer
- external identifiers
-
- scopus:84945952760
- ISSN
- 0302-9743
- ISBN
- 978-3-319-23132-7
- 978-3-319-23131-0
- DOI
- 10.1007/978-3-319-23132-7_28
- language
- English
- LU publication?
- no
- id
- e4a3881a-0566-4dfa-a411-19ff9c0f0102
- date added to LUP
- 2017-06-02 19:08:22
- date last changed
- 2024-09-17 02:02:38
@inproceedings{e4a3881a-0566-4dfa-a411-19ff9c0f0102, abstract = {{In the present article, we address the problem of automatic gender classification of web blog authors. More specifically, we employ eight widely used machine learning algorithms, in order to study the effectiveness of feature selection on improving the accuracy of gender classification. The feature ranking is performed over a set of statistical, part-of-speech tagging and language model features. In the experiments, we employed classification models based on decision trees, support vector machines and lazy-learning algorithms. The experimental evaluation performed on blog author gender classification data demonstrated the importance of language model features for this task and that feature selection significantly improves the accuracy of gender classification, regardless of the type of the machine learning algorithm used.}}, author = {{Aravantinou, Christina and Simaki, Vasiliki and Mporas, Iosif and Megalooikonomou, Vasileios}}, booktitle = {{Speech and Computer : 17th International Conference, SPECOM 2015, Athens, Greece, September 20-24, 2015, Proceedings}}, editor = {{Ronzhin, Andrey and Potapova, Rodmonga and Fakotakis, Nikos}}, isbn = {{978-3-319-23132-7}}, issn = {{0302-9743}}, keywords = {{Text classification; Gender identification; Feature selection}}, language = {{eng}}, pages = {{226--233}}, publisher = {{Springer}}, series = {{Lecture Notes in Computer Science}}, title = {{Gender Classification of Web Authors Using Feature Selection and Language Models}}, url = {{http://dx.doi.org/10.1007/978-3-319-23132-7_28}}, doi = {{10.1007/978-3-319-23132-7_28}}, volume = {{9319}}, year = {{2015}}, }