A network-based CNN model to identify the hidden information in text data

Yanyan Liu, Keping Li, Dongyang Yan and Shuang Gu

Physica A: Statistical Mechanics and its Applications, 2022, vol. 590, issue C

Abstract: With the development of the internet and big data, the missing or hidden information identification of text data has become an imperative task. At present, the challenge in the hidden information study is judging whether there is hidden information and where it exists. In this paper, hidden information refers to the words that do not appear in a sentence, however, they have certain correlations with the existing words or sentence and have a great influence on the comprehension of a sentence or part of the text data. This paper focuses on discovering the key and influential hidden information in the text data. A keyword-based hidden information extraction framework is proposed in this paper to search hidden entities, with the assumption that the importance of hidden objects is reflected by the keywords in the text data. A network-based Convolution Neural Network (CNN) model is developed to identify the hidden information related to keywords. The model is based on the results of CNN, and cosine similarity is used to judge whether there is hidden information in the source text data or not. We primarily form the word co-occurrence network of text, select the words with the highest degree as keywords, and generate random walk paths on the network. Besides, we use the random walk path where the last word is the keyword to train CNN. In the experimental section, the proposed model is applied to the dataset in 20Newgroups. The results show that the proposed model can effectively identify the hidden information associated with the keywords in the source text data, and the detection accuracy of keywords can reach 98%–99% achieved by CNN.

Keywords: Text data; Hidden information detection; Network model; Random walk; CNN (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:590:y:2022:i:c:s0378437121009444

DOI: 10.1016/j.physa.2021.126744

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