RETRACTED: Sentiment analysis and sarcasm detection from social network to train health-care professionals
ISSN: 1708-5284
Article publication date: 8 June 2021
Issue publication date: 22 February 2022
Retraction statement
The publishers of World Journal of Engineering wish to retract the article Godara, J., Aron, R. and Shabaz, M. (2022), "Sentiment analysis and sarcasm detection from social network to train health-care professionals", World Journal of Engineering, Vol. 19 No. 1, pp. 124-133. https://doi.org/10.1108/WJE-02-2021-0108
An internal investigation into a series of submissions has uncovered evidence that the peer review process was compromised. As a result of these concerns, the findings of the article cannot be relied upon. This decision has been taken in accordance with Emerald's publishing ethics and the COPE guidelines on retractions. The authors of this article would like to note that they do not agree with the content of this notice.
The publishers of the journal sincerely apologize to the readers.
Abstract
Purpose
Sentiment analysis has observed a nascent interest over the past decade in the field of social media analytics. With major advances in the volume, rationality and veracity of social networking data, the misunderstanding, uncertainty and inaccuracy within the data have multiplied. In the textual data, the location of sarcasm is a challenging task. It is a different way of expressing sentiments, in which people write or says something different than what they actually intended to. So, the researchers are showing interest to develop various techniques for the detection of sarcasm in the texts to boost the performance of sentiment analysis. This paper aims to overview the sentiment analysis, sarcasm and related work for sarcasm detection. Further, this paper provides training to health-care professionals to make the decision on the patient’s sentiments.
Design/methodology/approach
This paper has compared the performance of five different classifiers – support vector machine, naïve Bayes classifier, decision tree classifier, AdaBoost classifier and K-nearest neighbour on the Twitter data set.
Findings
This paper has observed that naïve Bayes has performed the best having the highest accuracy of 61.18%, and decision tree performed the worst with an accuracy of 54.27%. Accuracy of AdaBoost, K-nearest neighbour and support vector machine measured were 56.13%, 54.81% and 59.55%, respectively.
Originality/value
This research work is original.
Keywords
Citation
Godara, J., Aron, R. and Shabaz, M. (2022), "RETRACTED: Sentiment analysis and sarcasm detection from social network to train health-care professionals", World Journal of Engineering, Vol. 19 No. 1, pp. 124-133. https://doi.org/10.1108/WJE-02-2021-0108
Publisher
:Emerald Publishing Limited
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