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The purpose of this study is to develop the dictionary with grammar and multiword structure has to be used in conjunction with sentiment analysis to investigate the relationship between financial news and stock market volatility.

An algorithm has been developed for calculating the sentiment orientation and score of data with added information, and the results of calculation have been integrated to construct an empirical model for calculating stock market volatility.

The experimental results reveal a statistically significant relationship between financial news and stock market volatility. Moreover, positive (negative) news is found to be positively (negatively) correlated with positive stock returns, and the score of added information of the news is positively correlated with stock returns. Model verification and stock market volatility predictions are verified over four time periods (monthly, quarterly, semiannually and annually). The results show that the prediction accuracy of the models approaches 66% and stock market volatility with a particular trend-predicting effect in specific periods by using moving window evaluation.

Only one news source is used and the research period is only two years; thus, future studies should incorporate several data sources and use a longer period to conduct a more in-depth analysis.

Understanding trends in stock market volatility can decrease risk and increase profit from investment. Therefore, individuals or businesses can feasibly engage in investment activities for profit by understanding volatility trends in capital markets.

The ability to exploit textual information could potentially increase the quality of the data. Few scholars have applied sentiment analysis in investigating interdisciplinary topics that cover information management technology, accounting and finance. Furthermore, few studies have provided support for structured and unstructured data. In this paper, the efficiency of providing the algorithm, the model and the trend in stock market volatility has been demonstrated.

User-centered product designs have been attracting increasing attention, particularly in digital design. In interacting with the design support system, designers may face problems such as changing demands (e.g. unclear demands) and insufficient descriptions of these demands (e.g. data scarcity). The purpose of this paper is to build a design support system prototype for demonstrating the feasibility of meeting the high involvement of users in digital products.

Interactive evolutionary computation is applied.

A prototype of self-design greeting card system (SDGCS) was proposed. It provides professional design layouts, offers users numerous self-design models, and allows nonprofessional users to easily design greeting cards. The results of this study show that users were satisfied with the functionality, usefulness, and ease-of-use of the SDGCS.

This study used digital card design as an example for demonstrating the feasibility of satisfying the unclear needs of uses, enabling users to design a digital card creatively and complete their designs quickly. However, the current system only supports the design of static objects and layout of card. And the evaluation sample size was small, which might affect generalizability of the findings.

In practice, greeting card web operators can image the feasible business models by providing the attraction of self-design functionalities.

In current human-centric marketing era, consumers have begun to request interaction with designers in creating the value of a product. However, very few previous studies have provided support for digital product self-design. This study demonstrated the feasibility of satisfying the needs of self-design.

This study used the industry-oriented capstone course to increase the employability of electrical engineering and computer science (EECS) students in technological university.

In this study, EECS students were selected and divided into groups, and the non-equivalent pretest–posttest quasi-experimental research method was adopted.

Industry-oriented capstone courses can improve students' employability, especially general ability, behaviour and attitude.

The results of this study and many other studies show that capstone courses are helpful for the soft skills of students.

This study provides evidence that industry-oriented capstone courses can improve EECS students' employability.