Monireh Ebrahimi, Amir Hossein Yazdavar, Naomie Salim and Safaa Eltyeb
Many opinion-mining systems and tools have been developed to provide users with the attitudes of people toward entities and their attributes or the overall polarities of…
Abstract
Purpose
Many opinion-mining systems and tools have been developed to provide users with the attitudes of people toward entities and their attributes or the overall polarities of documents. In addition, side effects are one of the critical measures used to evaluate a patient’s opinion for a particular drug. However, side effect recognition is a challenging task, since side effects coincide with disease symptoms lexically and syntactically. The purpose of this paper is to extract drug side effects from drug reviews as an integral implicit-opinion words.
Design/methodology/approach
This paper proposes a detection algorithm to a medical-opinion-mining system using rule-based and support vector machines (SVM) algorithms. A corpus from 225 drug reviews was manually annotated by a medical expert for training and testing.
Findings
The results show that SVM significantly outperforms a rule-based algorithm. However, the results of both algorithms are encouraging and a good foundation for future research. Obviating the limitations and exploiting combined approaches would improve the results.
Practical implications
An automatic extraction for adverse drug effects information from online text can help regulatory authorities in rapid information screening and extraction instead of manual inspection and contributes to the acceleration of medical decision support and safety alert generation.
Originality/value
The results of this study can help database curators in compiling adverse drug effects databases and researchers to digest the huge amount of textual online information which is growing rapidly.
Details
Keywords
Taiseer Abdalla Elfadil Eisa, Naomie Salim and Salha Alzahrani
– The purpose of this paper is to analyse the state-of-the-art techniques used to detect plagiarism in terms of their limitations, features, taxonomies and processes.
Abstract
Purpose
The purpose of this paper is to analyse the state-of-the-art techniques used to detect plagiarism in terms of their limitations, features, taxonomies and processes.
Design/methodology/approach
The method used to execute this study consisted of a comprehensive search for relevant literature via six online database repositories namely; IEEE xplore, ACM Digital Library, ScienceDirect, EI Compendex, Web of Science and Springer using search strings obtained from the subject of discussion.
Findings
The findings revealed that existing plagiarism detection techniques require further enhancements as existing techniques are incapable of efficiently detecting plagiarised ideas, figures, tables, formulas and scanned documents.
Originality/value
The contribution of this study lies in its ability to have exposed the current trends in plagiarism detection researches and identify areas where further improvements are required so as to complement the performances of existing techniques.