2022
DOI: 10.3390/app12168105
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Cluster-Based Knowledge Graph and Entity-Relation Representation on Tourism Economical Sentiments

Abstract: The tourism industry has experienced fast and sustainable growth over the years in the economic sector. The data available online on the ever-growing tourism sector must be given importance as it provides crucial economic insights, which can be helpful for consumers and governments. Natural language processing (NLP) techniques have traditionally been used to tackle the issues of structuring of unprocessed data, and the representation of the data in a knowledge-based system. NLP is able to capture the full rich… Show more

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Cited by 14 publications
(6 citation statements)
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“…The pre-processing procedure seeks to enhance the data's quality and produce customised datasets from the original dataset in accordance with requirements in order to get better outcomes [39,40,41]. Our aim in this work is to use single-label classification method to improve the robustness of the deep learning model and attain higher accuracy.…”
Section: Pre-processing For Customized Daisee Dataset Creationmentioning
confidence: 99%
“…The pre-processing procedure seeks to enhance the data's quality and produce customised datasets from the original dataset in accordance with requirements in order to get better outcomes [39,40,41]. Our aim in this work is to use single-label classification method to improve the robustness of the deep learning model and attain higher accuracy.…”
Section: Pre-processing For Customized Daisee Dataset Creationmentioning
confidence: 99%
“…K-means clustering was employed to group districts based on vaccination rates, using random centroid initialization [29]. The Silhouette score method was then used to evaluate cluster quality, with scores ranging from −1 to +1, where higher values indicate better intra-cluster matching and sub-optimal inter-cluster matching [30].…”
Section: Cluster and Regression Analysismentioning
confidence: 99%
“…It is able to identify the natural groupings and patterns in the data without establishing any rules or criteria beforehand and discovering the patterns that might not be apparent from prior knowledge. Therefore, with the help of the clustering algorithms, the underlying patterns and trends for marketing and advertising motives with the huge amount of data can be used to classify the audience into meaningful categories [26].…”
Section: Audience Segmentationmentioning
confidence: 99%