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• W2786755062 abstract "Sentiment based relations are considered as important clues in order to identify the hidden links between medical concepts as well as to link various concepts with their source of glosses represented as descriptive explanations. Affinity score describes what extend two concepts are linked with each other by measuring the number of common sentiment words whereas gravity score identifies the sentimentoriented relevance between medical concepts and their various glosses. To uncover salient connections between the concepts, we employ the existing WordNet of Medical Events (WME 2.0) lexicon and enrich it with the affinity and gravity scores. We have employed the supervised classifiers, Naive Bayes and Sequential Minimal Optimization as well as unsupervised K-Means classifier to evaluate the identified sentiments of medical concepts present in WME 2.0 lexicon. The comparative results provide Cohen's kappa score of 0.73 and 0.78 before and after employing the affinity and gravity features into account. The agreement analysis was performed by medical practitioners. The higher agreement score indicates the positive influence of implementing the affinity and gravity features in determining sentiment relations. A further validation of the affinity and gravity features with the WME 2.0, three machine learning classifiers provide improved F-Measure scores such as 0.915, 0.915, and 0.974, which is much higher than the scores obtained without these features." @default.
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• W2786755062 date "2017-11-01" @default.
• W2786755062 modified "2023-09-23" @default.
• W2786755062 title "Employing sentiment-based affinity and gravity scores to identify relations of medical concepts" @default.
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• W2786755062 doi "https://doi.org/10.1109/ssci.2017.8285275" @default.
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