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Role of sentiment analysis in social media security and analytics

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Abstract Social media, in recent times, has with eased an explosion of data with so many social media platforms available to interact and express opinions freely. This has led to easy access to the privacy of social media users which raise broader security concerns and issues. The present paper provides an overview of various sentiment analysis approaches and techniques for social media security and analytics. The multiple security application domains like deception detection, anomaly detection, risk management, and disaster relief have been identified where sentiment analysis is used for social media security. An in‐depth study on security issues related to data provenance, distrust, e‐commerce security, consumer security breaches, market surveillance, credibility, and risk assessment in social media have been presented. A comparison of various techniques, methodologies, dataset, and application domain where sentiment analysis is used has been discussed. The present work discusses the results of different machine learning techniques based on the performance metrics that have been used for the implementation of sentiment analysis in the respective security domains. It identifies the various gaps, issues, and the recent advancements in the field and presents a line of work that needs to be carried forward in future. This article is categorized under: Commercial, Legal, and Ethical Issues > Security and Privacy Technologies > Machine Learning Technologies > Prediction
Organization of the review depicting the security perspective, security challenges, and sentiment analysis approaches that can be used for detecting security breaches in social media
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Percentage of each data source in various categories of security domain with its performance measures
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Percentage of studies conducted in each category of security domain
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Detection process model for social media security: where data is extracted, preprocessed, identification of security challenge, and application of sentiment analysis technique to produce the desired results and finally perform prediction
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Machine learning algorithms used for sentiment classification which includes supervised learning, unsupervised learning, semi‐supervised learning and ensemble or meta classifiers
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Technologies > Prediction
Technologies > Machine Learning
Commercial, Legal, and Ethical Issues > Security and Privacy

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