A Weighted ML-KNN Model for Predicting Users’Personality Traits
- 10.2991/isca-13.2013.58How to use a DOI?
- Big-5 personality traits; personality prediction; ML-kNN; weighted feature; information entropy theory
Gaining insight into human personality and its impact on human behavior is very valuable in many applications, such as web information credibility prediction. In this paper, we explore using weighted ML-kNN model for automatic recognition of personality traits of web users, based on a given composition text. After extracting features through analysis of the content of user’s Essays statues updates, we discretize contiguous attribute using Kohonen’s feature-map algorithm, and assign weight to extracted features based on information entropy. The Essays dataset is partitioned into training dataset and test dataset. For a given test user, the weighted distance between test user and training user is calculated, and based on which the nearest neighbors are identified. The personality traits of test user are then predicted by using ML-kNN algorithm. Our experiment on the Essays dataset shows expected positive results.
- © 2013, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Xianglin Zuo AU - Baoping Feng AU - Yuan Yao AU - Tianyi Zhang AU - Qian Zhang AU - Mengmeng Wang AU - Wanli Zuo PY - 2013/10 DA - 2013/10 TI - A Weighted ML-KNN Model for Predicting Users’Personality Traits BT - Proceedings of 2013 International Conference on Information Science and Computer Applications PB - Atlantis Press SP - 345 EP - 350 SN - 1951-6851 UR - https://doi.org/10.2991/isca-13.2013.58 DO - 10.2991/isca-13.2013.58 ID - Zuo2013/10 ER -