Opinion mining for national security: techniques, domain applications, challenges and research opportunities

Abstract

Background Opinion mining, or sentiment analysis, is a field in Natural Language Processing (NLP). It extracts people’s thoughts, including assessments, attitudes, and emotions toward individuals, topics, and events. The task is technically challenging but incredibly useful. With the explosive growth of the digital platform in cyberspace, such as blogs and social networks, individuals and organisations are increasingly utilising public opinion for their decision-making. In recent years, significant research concerning mining people’s sentiments based on text in cyberspace using opinion mining has been explored. Researchers have applied numerous opinions mining techniques, including machine learning and lexicon-based approach to analyse and classify people’s sentiments based on a text and discuss the existing gap. Thus, it creates a research opportunity for other researchers to investigate and propose improved methods and new domain applications to fill the gap. Methods In this paper, a structured literature review has been done by considering 122 articles to examine all relevant research accomplished in the field of opinion mining application and the suggested Kansei approach to solve the challenges that occur in mining sentiments based on text in cyberspace. Five different platforms database were systematically searched between 2015 and 2021: ACM (Association for Computing Machinery), IEEE (Advancing Technology for Humanity), SCIENCE DIRECT, SpringerLink, and SCOPUS. Results This study analyses various techniques of opinion mining as well as the Kansei approach that will help to enhance techniques in mining people’s sentiment and emotion in cyberspace. Most of the study addressed methods including machine learning, lexicon-based approach, hybrid approach, and Kansei approach in mining the sentiment and emotion based on text. The possible societal impacts of the current opinion mining technique, including machine learning and the Kansei approach, along with major trends and challenges, are highlighted. Conclusion Various applications of opinion mining techniques in mining people’s sentiment and emotion according to the objective of the research, used method, dataset, summarized in this study. This study serves as a theoretical analysis of the opinion mining method complemented by the Kansei approach in classifying people’s sentiments based on text in cyberspace. Kansei approach can measure people’s impressions using artefacts based on senses including sight, feeling and cognition reported precise results for the assessment of human emotion. Therefore, this research suggests that the Kansei approach should be a complementary factor including in the development of a dictionary focusing on emotion in the national security domain. Also, this theoretical analysis will act as a reference to researchers regarding the Kansei approach as one of the techniques to improve hybrid approaches in opinion mining.

Decentralized Access Control using Blockchain Technology for Application in Smart Farming

“…This paper examines all applicable articles related to decentralized access control for IoT environment application. The method described in [8] was utilised to choose the most important articles related to this research objectives. Articles filtering was done using the six filters that are defined in Fig.…”

The Integration of Kansei Engineering and Kano Model on Natural Language Processing (NLP) to Support Development of Service Product in the Borobudur Temple Tourism

“…This was followed by the integration of the lexiconbased method, a dictionary of opinion words was made to identify positive and negative words in a sentence. Lexiconbased sentiment analysis is a method of using a dictionary that integrates the polarity of words to determine sentiment [47]. Furthermore, lexicon-based is capable of extracting opinion sentences with very high precision, conveying the language's permanent semantic features as well as the collocational properties of certain text [31].…”

Political Security Threat Prediction Framework Using Hybrid Lexicon-Based Approach and Machine Learning Technique

“…It differs from the NRC emotion intensity lexicon which can only classify text as either positive or negative emotions and sentiments. The NRC word-emotion association lexicon cannot tell if a piece of text is positive or negative since its English words list is linked to only eight basic emotions (anger, anticipation, disgust, fear, joy, sadness, surprise and trust) [2]. Since no dictionary for political security is available, we applied the existing NRC emotion lexicon in this research and combined this approach with selected machine learning techniques.…”

“…Since information shared in cyberspace is frequently embedded with emotions that may contain national security threats (according to each element of national security), real-time detection of disruptive emotions plays a key role in helping authorities manage the situation early. Various gaps, techniques and domain applications that focus on existing opinion mining methods (such as the lexicon-based approach and machine learning techniques) can be used to determine the existing sentiments embedded in sentences throughout several domains, as discussed in [2].…”