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The development of IoT technologies and the massive admiration and acceptance of social media tools and applications, new doors of opportunity have been opened for using data analytics in gaining meaningful insights from unstructured information. The application of opinion mining and sentiment analysis (OMSA) in the era of big data have been used a useful way in categorizing the opinion into different sentiment and in general evaluating the mood of the public. Moreover, different techniques of OMSA have been developed over the years in different data sets and applied to various experimental settings. In this regard, this paper presents a comprehensive systematic literature review, aims to discuss both technical aspect of OMSA (techniques and types) and non-technical aspect in the form of application areas are discussed. Furthermore, this paper also highlighted both technical aspects of OMSA in the form of challenges in the development of its technique and non-technical challenges mainly based on its application. These challenges are presented as a future direction for research.

This research intends to examine how spending time on Facebook affect students' academic performance. It explores behavioural factors affecting students utilising Facebook. Questionnaires were personally distributed to 667 undergraduate and postgraduate students from the University of Malaya to determine their intentions for using Facebook, which will allow us to discern the symptoms related to excessive Facebook usage by students. The AMOS software was used to hypothesise the relationship between the variables. The results show that students use Facebook for entertainment, relaxing/escaping, and passing time, but not for socialising and social information. Spending time on Facebook has a positive effect on their academic performance, which indicated that the time spent on Facebook did not affect their education. It is anticipated that the findings from this study would provide clear insights on how the behavioural factors of a student affect their academic performance. This research would help educationists, parents, and the students themselves development appropriate measures to control and curb excessive usage of Facebook or using Facebook as a learning tool.

Real financial time series data always exhibit structural change, jumps or breaks. Thus, in this paper, the performance of the linear vector autoregressive model (VAR), mean adjusted Markov switching vector autoregressive model (MSM-VAR) and mean adjusted heteroskedasticity Markov switching vector autoregressive model (MSMH-VAR) are applied in order to examine the oil price return and the gold price return effect on stock market returns. The two break point tests indicate the existence of break dates in the data. In addition, a comparison among the three model's performance show that the two Markov switching vector autoregressive models with first autoregressive order able to provide the most significance, reliable and valid results as compared to linear vector autoregressive.

Stationary Test is an important test in detect the time series behavior since financial and economic data series always have missing data, structural change as well as jumps or breaks in the data set. Moreover, stationary test is able to transform the nonlinear time series variable to become stationary by taking difference-stationary process or trend-stationary process. Two different types of hypothesis testing of stationary tests that are Augmented Dickey-Fuller (ADF) test and Kwiatkowski-Philips-Schmidt-Shin (KPSS) test are examine in this paper to describe the properties of the time series variables in financial model. Besides, Least Square method is used in Augmented Dickey-Fuller test to detect the changes of the series and Lagrange multiplier is used in Kwiatkowski-Philips-Schmidt-Shin test to examine the properties of oil price, gold price and Malaysia stock market. Moreover, Quandt-Andrews, Bai-Perron and Chow tests are also use to detect the existence of break in the data series. The monthly inde...

Gold is a popular precious metal where the demand is driven not only for practical use but also as a popular investments commodity. While stock market represents a country growth, thus gold price effect on stock market behavior as interest in the study. Markov Switching Vector Error Correction Models are applied to analysis the relationship between gold price and stock market changes since real financial data always exhibit regime switching, jumps or missing data through time. Besides, there are numerous specifications of Markov Switching Vector Error Correction Models and this paper will compare the intercept adjusted Markov Switching Vector Error Correction Model and intercept adjusted heteroskedasticity Markov Switching Vector Error Correction Model to determine the best model representation in capturing the transition of the time series. Results have shown that gold price has a positive relationship with Malaysia, Thailand and Indonesia stock market and a two regime intercept adjusted heteroskedasticity Markov Switching Vector Error Correction Model is able to provide the more significance and reliable result compare to intercept adjusted Markov Switching Vector Error Correction Models.

The increases of demand on knowledge-based and production-based market force the growth of higher education. International students' enrollment contributes to economic growth and increase country's income, university reputation and name; promote the competitive of education and training markets. This paper used multilevel analysis to study the international students' enrollment in Malaysia public university. Student's background variables and institution background variables were study in this paper and the relationship among them also been investigated. Result shows that institution type is a significance factor on international students' enrollment in Malaysia public university.