Standard
Age Identification of Twitter Users: Classification Methods and Sociolinguistic Analysis. /
Simaki, Vasiliki; Mporas, Iosif; Megalooikonomou, Vasileios.
Computational Linguistics and Intelligent Text Processing : 17th International Conference, CICLing 2016, Konya, Turkey, April 3–9, 2016, Revised Selected Papers, Part II. ed. / A. Gelbukh. Cham: Springer, 2016. p. 385-395 (Lecture Notes in Computer Science; Vol. 9624).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Simaki, V, Mporas, I & Megalooikonomou, V 2016,
Age Identification of Twitter Users: Classification Methods and Sociolinguistic Analysis. in A Gelbukh (ed.),
Computational Linguistics and Intelligent Text Processing : 17th International Conference, CICLing 2016, Konya, Turkey, April 3–9, 2016, Revised Selected Papers, Part II. Lecture Notes in Computer Science, vol. 9624, Springer, Cham, pp. 385-395.
https://doi.org/10.1007/978-3-319-75487-1_30
APA
Simaki, V., Mporas, I., & Megalooikonomou, V. (2016).
Age Identification of Twitter Users: Classification Methods and Sociolinguistic Analysis. In A. Gelbukh (Ed.),
Computational Linguistics and Intelligent Text Processing : 17th International Conference, CICLing 2016, Konya, Turkey, April 3–9, 2016, Revised Selected Papers, Part II (pp. 385-395). (Lecture Notes in Computer Science; Vol. 9624). Springer.
https://doi.org/10.1007/978-3-319-75487-1_30
Vancouver
Simaki V, Mporas I, Megalooikonomou V.
Age Identification of Twitter Users: Classification Methods and Sociolinguistic Analysis. In Gelbukh A, editor, Computational Linguistics and Intelligent Text Processing : 17th International Conference, CICLing 2016, Konya, Turkey, April 3–9, 2016, Revised Selected Papers, Part II. Cham: Springer. 2016. p. 385-395. (Lecture Notes in Computer Science). doi: 10.1007/978-3-319-75487-1_30
Author
Simaki, Vasiliki ; Mporas, Iosif ; Megalooikonomou, Vasileios. /
Age Identification of Twitter Users : Classification Methods and Sociolinguistic Analysis. Computational Linguistics and Intelligent Text Processing : 17th International Conference, CICLing 2016, Konya, Turkey, April 3–9, 2016, Revised Selected Papers, Part II. editor / A. Gelbukh. Cham : Springer, 2016. pp. 385-395 (Lecture Notes in Computer Science).
Bibtex
@inproceedings{fc9da39910824261898d60966cae66ea,
title = "Age Identification of Twitter Users: Classification Methods and Sociolinguistic Analysis",
abstract = "In this article, we address the problem of age identification of Twitter users, after their online text. We used a set of text mining, sociolinguistic-based and content-related text features, and we evaluated a number of well-known and widely used machine learning algorithms for classification, in order to examine their appropriateness on this task. The experimental results showed that Random Forest algorithm offered superior performance achieving accuracy equal to 61%. We ranked the classification features after their informativity, using the ReliefF algorithm, and we analyzed the results in terms of the sociolinguistic principles on age linguistic variation.",
author = "Vasiliki Simaki and Iosif Mporas and Vasileios Megalooikonomou",
year = "2016",
doi = "10.1007/978-3-319-75487-1_30",
language = "English",
isbn = "9783319754864",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "385--395",
editor = "A. Gelbukh",
booktitle = "Computational Linguistics and Intelligent Text Processing",
}
RIS
TY - GEN
T1 - Age Identification of Twitter Users
T2 - Classification Methods and Sociolinguistic Analysis
AU - Simaki, Vasiliki
AU - Mporas, Iosif
AU - Megalooikonomou, Vasileios
PY - 2016
Y1 - 2016
N2 - In this article, we address the problem of age identification of Twitter users, after their online text. We used a set of text mining, sociolinguistic-based and content-related text features, and we evaluated a number of well-known and widely used machine learning algorithms for classification, in order to examine their appropriateness on this task. The experimental results showed that Random Forest algorithm offered superior performance achieving accuracy equal to 61%. We ranked the classification features after their informativity, using the ReliefF algorithm, and we analyzed the results in terms of the sociolinguistic principles on age linguistic variation.
AB - In this article, we address the problem of age identification of Twitter users, after their online text. We used a set of text mining, sociolinguistic-based and content-related text features, and we evaluated a number of well-known and widely used machine learning algorithms for classification, in order to examine their appropriateness on this task. The experimental results showed that Random Forest algorithm offered superior performance achieving accuracy equal to 61%. We ranked the classification features after their informativity, using the ReliefF algorithm, and we analyzed the results in terms of the sociolinguistic principles on age linguistic variation.
U2 - 10.1007/978-3-319-75487-1_30
DO - 10.1007/978-3-319-75487-1_30
M3 - Conference contribution/Paper
SN - 9783319754864
T3 - Lecture Notes in Computer Science
SP - 385
EP - 395
BT - Computational Linguistics and Intelligent Text Processing
A2 - Gelbukh, A.
PB - Springer
CY - Cham
ER -
