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.

The internet offers a powerful medium for expressing opinions, emotions and ideas, using online platforms supported by smartphone usage and high internet penetration. Most internet posts are textual based and can include people's emotional feelings for a particular moment or sentiment. Monitoring online sentiments or opinions is important for detecting any excessive emotions triggered by citizens which can lead to unintended consequences and threats to national security. Riots and civil war, for instance, must be addressed due to the risk of jeopardizing social stability and political security, which are crucial elements of national security. Mining opinions according to the national security domain is a relevant research topic that must be enhanced. Mechanisms and techniques that can mine opinions in the aspect of political security require significant improvements to obtain optimum results. Researchers have noted that there is a strong relationship between emotion, sentiment and political security threats. This study proposes a new theoretical framework for predicting political security threats using a hybrid technique: the combination of lexicon-based approach and machine learning in cyberspace. In the proposed framework, Decision Tree, Naive Bayes, and Support Vector Machine have been deployed as threat classifiers. To validate our proposed framework, an experimental analysis is accomplished. The performance of each technique used in the experiments is reported. In this study, our proposed framework reveals that the hybrid Lexicon-based approach with the Decision Tree classifier recorded the highest performance score for predicting political security threats. These findings offer valuable insight to ongoing research on opinion mining in predicting threats based on the political security domain.

The advancement of technology has led to the development of an artificial intelligence-based healthcare-related application that can be easily accessed and used to assist people in lifestyle intervention for preventing the development of noncommunicable diseases (NCDs). Previous research suggested that users are demanding a more emotional evocative user interface design. However, most of the time, it has been ignored due to lack of a model that could be referred in developing emotional evocative user interface design. This creates a gap in the user interface design that could lead to the ineffectiveness of content delivery in the NCD domain. This paper aims to investigate emotion traits and their relationship with user interface design for lifestyle intervention. Kansei Engineering method was applied to determine the dimensions for constructing emotional evocative user interface design. Data analysis was done using SPSS statistic tool and the result showed the emotional concepts that are significant and impactful towards user interface design for lifestyle intervention in NCD domain. The outcome of this research shall create new research fields that incorporate multi research domain including user interface design and emotions.

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Securing a nation is more complicated in modern days than how it was decades ago. In the era of big data, massive information is constantly being shared in cyberspace. Online rumours and fake news could evoke negative emotions and disruptive behaviours that possibly can jeopardize national security. Real-time detection and monitoring of unsettling emotions and potential national security threats should be further developed to help authorities manage the situation early. Text in the online news could be weighted with emotions that possibly lead to a misunderstanding that can affect national security and trigger chaos. Thus, understanding the emotion included in the online news and the relationship with national security is crucial. Kansei approach was determined as a methodology capable of interpreting human emotions towards an artefact. This research explores the emotion assessment using Kansei for text in online news and summarized the emotion variable factors that are likely to have a relationship with an individual state of mind towards one of the national security elements which are political security. The result determines that the identified variables of factors were "Frustrated," Consent," Resentful" and "Attentive". This gives an understanding of the significant effect of people's emotions represented in the text for political security elements.