The latent learning model to derive semantic relations of words from unstructured text data in social media

The latent learning model to derive semantic relations of words from unstructured text data in... Multimed Tools Appl https://doi.org/10.1007/s11042-018-6211-2 The latent learning model to derive semantic relations of words from unstructured text data in social media 1 1 1 Jiwan Seo & Karam Yoo & Seungjin Choi & 1 1 Yura Alex Kim & Sangyong Han Received: 7 December 2017 /Revised: 2 May 2018 /Accepted: 23 May 2018 Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Unstructured text data is very important in many applications because it reflects the thought of the people who create this data. However, it is difficult to realize the latent information as it was hidden on the unstructured text data. This paper proposes a latent learning method to construct the lexical structure to constitute the relations between the latent meaning and words. The established lexical structure derived the useful information from unstructured text data and this information and this information can be used for various application. This paper describes how to predict a rating from user-written reviews which is one of unstructured text data. And it also provides visualization information of the semantic lexical structures as the result of analysis. As a result, the proposed method easily quantifies the semantic relations of words and it shows good http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

The latent learning model to derive semantic relations of words from unstructured text data in social media

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-018-6211-2
Publisher site
See Article on Publisher Site

Abstract

Multimed Tools Appl https://doi.org/10.1007/s11042-018-6211-2 The latent learning model to derive semantic relations of words from unstructured text data in social media 1 1 1 Jiwan Seo & Karam Yoo & Seungjin Choi & 1 1 Yura Alex Kim & Sangyong Han Received: 7 December 2017 /Revised: 2 May 2018 /Accepted: 23 May 2018 Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Unstructured text data is very important in many applications because it reflects the thought of the people who create this data. However, it is difficult to realize the latent information as it was hidden on the unstructured text data. This paper proposes a latent learning method to construct the lexical structure to constitute the relations between the latent meaning and words. The established lexical structure derived the useful information from unstructured text data and this information and this information can be used for various application. This paper describes how to predict a rating from user-written reviews which is one of unstructured text data. And it also provides visualization information of the semantic lexical structures as the result of analysis. As a result, the proposed method easily quantifies the semantic relations of words and it shows good

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: Jun 3, 2018

References

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