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

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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 performance on prediction of ratings from unstructured text data. The proposed method can contribute to analyzing the unstructured text data in various perspectives on latent meaning of words.

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Acknowledgements

This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT and future Planning (NRF - 2015R1A2 A2A01005304) and this research was supported by the Chung-Ang University Graduate Research Scholarship in 2015.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Chung-Ang University, 47, Heukeok-ro, Seoul, Republic of Korea

    Jiwan Seo, Karam Yoo, Seungjin Choi, Yura Alex Kim & Sangyong Han

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  1. Jiwan Seo
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  2. Karam Yoo
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  3. Seungjin Choi
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  4. Yura Alex Kim
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  5. Sangyong Han
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Correspondence to Sangyong Han.

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Seo, J., Yoo, K., Choi, S. et al. The latent learning model to derive semantic relations of words from unstructured text data in social media. Multimed Tools Appl 78, 28649–28663 (2019). https://doi.org/10.1007/s11042-018-6211-2

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