MCDM'20 - paper no. 1
DOMAIN SPECIFIC KEY FEATURE EXTRACTION USING KNOWLEDGE GRAPH MINING
Mohit Kumar Barai, Subhasis Sanyal
Abstract:
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:
feature extraction, natural language processing, product review, text processing, knowledge graph
Reference index:
Mohit Kumar Barai, Subhasis Sanyal, (2021), DOMAIN SPECIFIC KEY FEATURE EXTRACTION USING KNOWLEDGE GRAPH MINING, Multiple Criteria Decision Making (15), pp. 1-22
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