The popularity of big data analytics (BDA) has boosted the interest of organisations into exploiting their large scale data. This technology can become a strategic stimulation for organisations to achieve competitive advantage and sustainable growth. Previous BDA research, however, has focused more on introducing more traits, known as Vs for big data traits, while ignoring the quality of data when examining the application of BDA. Therefore, this study aims to explore the effect of big data traits and data quality dimensions on BDA application. This study has formulated 10 hypotheses that comprised of the relationships of big data traits, accuracy, believability, completeness, timeliness, ease of operation, and BDA application constructs. This study conducted a survey using a questionnaire as a data collection instrument. Then, the partial least squares structural equation modelling technique was used to analyse the hypothesised relationships between the constructs. The findings revealed that big data traits can significantly affect all constructs for data quality dimensions and that the ease of operation construct has a significant effect on BDA application. This study contributes to the literature by bringing new insights to the field of BDA and may serve as a guideline for future researchers and practitioners when studying BDA application.

The development of the Internet of Things (IoT) has produced new innovative solutions, such as smart cities, which enable humans to have a more efficient, convenient and smarter way of life. The Intelligent Transportation System (ITS) is part of several smart city applications where it enhances the processes of transportation and commutation. ITS aims to solve traffic problems, mainly traffic congestion. In recent years, new models and frameworks for predicting traffic flow have been rapidly developed to enhance the performance of traffic flow prediction, alongside the implementation of Artificial Intelligence (AI) methods such as machine learning (ML). To better understand how ML implementations can enhance traffic flow prediction, it is important to inclusively know the current research that has been conducted. The objective of this paper is to present a comprehensive and systematic review of the literature involving 39 articles published from 2016 onwards and extracted from four main databases: Scopus, ScienceDirect, SpringerLink and Taylor & Francis. The extracted information includes the gaps, approaches, evaluation methods, variables, datasets and results of each reviewed study based on the methodology and algorithms used for the purpose of predicting traffic flow. Based on our findings, the common and frequent machine learning techniques that have been applied for traffic flow prediction are Convolutional Neural Network and Long-Short Term Memory. The performance of their proposed techniques was compared with existing baseline models to determine their effectiveness. This paper is limited to certain literature pertaining to common databases. Through this limitation, the discussion is more focused on (and limited to) the techniques found on the list of reviewed articles. The aim of this paper is to provide a comprehensive understanding of the application of ML and DL techniques for improving traffic flow prediction, contributing to the betterment of ITS in smart cities. For future endeavours, experimental studies that apply the most used techniques in the articles reviewed in this study (such as CNN, LSTM or a combination of both techniques) can be accomplished to enhance traffic flow prediction. The results can be compared with baseline studies to determine the accuracy of these techniques.

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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.