Domain specific key feature extraction using knowledge graph mining

• Authors: Barai Mohit Kumar , Sanyal Subhasis
• Languages of publication: EN

Abstracts

EN

In the field of text mining, many novel feature extraction approaches have been propounded. The following research paper is based on a novel feature extraction algorithm. In this paper, to formulate this approach, a weighted graph mining has been used to ensure the effectiveness of the feature extraction and computational efficiency; only the most effective graphs representing the maximum number of triangles based on a predefined relational criterion have been considered. The proposed novel technique is an amalgamation of the relation between words surrounding an aspect of the product and the lexicon-based connection among those words, which creates a relational triangle. A maximum number of a triangle covering an element has been accounted as a prime feature. The proposed algorithm performs more than three times better than TF-IDF within a limited set of data in analysis based on domain-specific data.

Keywords

EN

Feature extraction; Knowledge graph; Natural language processing; Product review; Text processing

• Publisher: Uniwersytet Ekonomiczny w Katowicach
• Journal: Multiple Criteria Decision Making
• Year: 2020
• Volume: 15
• Pages: 1-22

Contributors

author

Barai Mohit Kumar

• Samsung Research Institute, Noida, India

author

Sanyal Subhasis

• Samsung Research Institute, Noida, India,

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Identifiers

ISSN

2084-1531