Word2vec-based Latent Semantic Indexing (Word2Vec-LSI) for Contextual Analysis in Job-Matching Application

Job-matching applications have become a technology that provides solutions for making decisions about accepting and looking for work. The contextual analysis of documents or data from job matching is needed to make decisions. Some existing studies on the analysis of job-matching applications can use...

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Bibliographic Details
Published in:International journal of advanced computer science & applications 2024, Vol.15 (3)
Main Authors: Sukri, Sukri, Samsudin, Noor Azah, Fadzrin, Ezak, Khalid, Shamsul Kamal Ahmad, Trisnawati, Liza
Format: Article
Language:English
Subjects:
Accuracy
Decision analysis
Indexing
Matching
Modelling
Natural language processing
Recall
Semantics
Similarity
Words (language)
Online Access:Get full text
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Summary:Job-matching applications have become a technology that provides solutions for making decisions about accepting and looking for work. The contextual analysis of documents or data from job matching is needed to make decisions. Some existing studies on the analysis of job-matching applications can use the Latent Semantic Indexing (LSI) method, which is based on word-to-word comparisons in the text. LSI has the advantage of contextual analysis. It can analyze amounts of data above 10,000 words. However, the conventional LSI method has limitations in contextual analysis because it uses the exact words but different meanings. Therefore, this paper proposes a new technique called word2vec-based latent semantic indexing (Word2vec-LSI) for contextual analysis, which is based on gen-sim as a multi-language word library. Then, modeling in text and wordnet and stopword as basic text modeling. We then used word2vec-LSI to perform contextual analysis based on the Irish (IE), Swedish (SE), and United Kingdom (UK) languages in the dataset (Jobs on CareerBuilder UK). The results of applying conventional LSI have an accuracy level of 79%, recall has a value of 79%, precision has a value of 62%, and Fi-Scor has a value of 70% with a similarity level of up to 50%. After implementing word2vec-LSI, it can increase accuracy, recall, and precision, and Fi-Scor both have 84% in contextual analysis, and the similarity level reaches up to 95%. Experiments confirm the usefulness of word2vec-LSI in increasing accuracy for contextual analysis applicable in natural language text mining.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2024.0150371