Age Prediction in Social Networks Based on Word Embedding and Tensor Learning
Ziyi Lin, Yan Wang
Available Online October 2016.
- https://doi.org/10.2991/ceie-16.2017.28How to use a DOI?
- Tensor Space Model; Word Embedding; Age Prediction; Tensor Learning; Social Network
- Latent attribute prediction problem in social network provides a set of conditions for the construction of text classification models. The general framework of current latent attribute prediction problems is mapping the text in social network to vector space, along with a classification model to classify different categories. Unfortunately, as the vector space model ignores the similarity and relevance between different words, it fails to identify the semantic fuzziness in natural language and always performs badly on a long text. With the aim of finding a better framework for age prediction problem, in this paper we propose a word embedding based tensor space model which maps text to tensor feature space. The proposed method relies on supervised tensor learning algorithms which are well studied by many scholars, thus allowing for its easy application in text classification problems. Two experiments on different testing sets show the effectiveness and limitation of our approach.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Ziyi Lin AU - Yan Wang PY - 2016/10 DA - 2016/10 TI - Age Prediction in Social Networks Based on Word Embedding and Tensor Learning BT - Proceedings of the International Conference on Communication and Electronic Information Engineering (CEIE 2016) PB - Atlantis Press SP - 213 EP - 222 SN - 2352-5401 UR - https://doi.org/10.2991/ceie-16.2017.28 DO - https://doi.org/10.2991/ceie-16.2017.28 ID - Lin2016/10 ER -