Relation Extraction of Medical Concepts Using Categorization and Sentiment Analysis

2018 ◽  
Vol 10 (4) ◽  
pp. 670-685 ◽  
Author(s):  
Anupam Mondal ◽  
Erik Cambria ◽  
Dipankar Das ◽  
Amir Hussain ◽  
Sivaji Bandyopadhyay
Keyword(s):  
Sentiment Analysis ◽  
Relation Extraction ◽  
Medical Concepts

scholarly journals Drug Disease Relation Extraction from Biomedical Literature Using NLP and Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wahiba Ben Abdessalem Karaa ◽  
Eman H. Alkhammash ◽  
Aida Bchir
Keyword(s):  
Language Processing ◽  
Relation Extraction ◽  
Relevant Information ◽  
Biomedical Literature ◽  
Semantic Relations ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Semantic Features ◽  
Semantic Relationships ◽  
Medical Concepts

Extracting the relations between medical concepts is very valuable in the medical domain. Scientists need to extract relevant information and semantic relations between medical concepts, including protein and protein, gene and protein, drug and drug, and drug and disease. These relations can be extracted from biomedical literature available on various databases. This study examines the extraction of semantic relations that can occur between diseases and drugs. Findings will help specialists make good decisions when administering a medication to a patient and will allow them to continuously be up to date in their field. The objective of this work is to identify different features related to drugs and diseases from medical texts by applying Natural Language Processing (NLP) techniques and UMLS ontology. The Support Vector Machine classifier uses these features to extract valuable semantic relationships among text entities. The contributing factor of this research is the combination of the strength of a suggested NLP technique, which takes advantage of UMLS ontology and enables the extraction of correct and adequate features (frequency features, lexical features, morphological features, syntactic features, and semantic features), and Support Vector Machines with polynomial kernel function. These features are manipulated to pinpoint the relations between drug and disease. The proposed approach was evaluated using a standard corpus extracted from MEDLINE. The finding considerably improves the performance and outperforms similar works, especially the f-score for the most important relation “cure,” which is equal to 98.19%. The accuracy percentage is better than those in all the existing works for all the relations.

scholarly journals An approach in health relation extraction

2017 ◽  
Vol 1 (Q3) ◽  
pp. 51-63
Author(s):  
Nghia Huu Huynh ◽  
Quoc Bao Ho ◽  
Te An Nguyen
Keyword(s):  
Treatment Process ◽  
Relation Extraction ◽  
Support Vector ◽  
Medical Field ◽  
Medical Problems ◽  
Rule Based ◽  
Health Care Practitioners ◽  
Rule Set ◽  
Semantic Dependencies ◽  
Medical Concepts

Extracting relations among medical concepts is very important in the medical field. The relations denote the events or the possible relations between the concepts. Information about these relations provides users with a full view of medical problems. This helps physicians and health-care practitioners make effective decisions and minimize errors in the treatment process. This paper collects methods for relations extraction in health texts and presents an approach on one type of specific relation (i.e. template filling). The approach combines methods including rule-based and machine learningbased. The rule-based method uses the relation of semantic dependencies among the concepts to extract the rule set. The machine learning-based method uses the SVM (Support Vector Machine) algorithm and a feature set proposed. The results of the approach were estimated on an accuracy of 0.849.

Study of Sentiment of Governor's Election Opinion in 2018

2018 ◽  
pp. 231-238
Author(s):  
Agung Eddy Suryo Saputro ◽  
Khairil Anwar Notodiputro ◽  
Indahwati A
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Addition Method ◽  
Sentiment Mining ◽  
Positive Sentiment ◽  
Trending Topics ◽  
Election News

In 2018, Indonesia implemented a Governor's Election which included 17 provinces. For several months before the Election, news and opinions regarding the Governor's Election were often trending topics on Twitter. This study aims to describe the results of sentiment mining and determine the best method for predicting sentiment classes. Sentiment mining is based on Lexicon. While the methods used for sentiment analysis are Naive Bayes and C5.0. The results showed that the percentage of positive sentiment in 17 provinces was greater than the negative and neutral sentiments. In addition, method C5.0 produces a better prediction than Naive Bayes.

An historical analysis of species references in American English

Corpora ◽  
2019 ◽  
Vol 14 (3) ◽  
pp. 327-349
Author(s):  
Craig Frayne
Keyword(s):  
Environmental Change ◽  
Sentiment Analysis ◽  
Quantitative Methods ◽  
English Language ◽  
Language Use ◽  
American English ◽  
Historical Analysis ◽  
The Past ◽  
Corpus Studies ◽  
Google Books

This study uses the two largest available American English language corpora, Google Books and the Corpus of Historical American English (coha), to investigate relations between ecology and language. The paper introduces ecolinguistics as a promising theme for corpus research. While some previous ecolinguistic research has used corpus approaches, there is a case to be made for quantitative methods that draw on larger datasets. Building on other corpus studies that have made connections between language use and environmental change, this paper investigates whether linguistic references to other species have changed in the past two centuries and, if so, how. The methodology consists of two main parts: an examination of the frequency of common names of species followed by aspect-level sentiment analysis of concordance lines. Results point to both opportunities and challenges associated with applying corpus methods to ecolinguistc research.

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