Growth in the area of opinion mining and sentiment analysis has been rapid and aims to explore the opinions or text present on different platforms of social media through machine-learning techniques with sentiment, subjectivity analysis or polarity calculations. Despite the use of various machine-learning techniques and tools for sentiment analysis during elections, there is a dire need for a state-of-the-art approach. To deal with these challenges, the contribution of this paper includes the adoption of a hybrid approach that involves a sentiment analyzer that includes machine learning. Moreover, this paper also provides a comparison of techniques of sentiment analysis in the analysis of political views by applying supervised machine-learning algorithms such as Naïve Bayes and support vector machines (SVM).

Psychology says not everyone is able to do all type of tasks assigned to them. This point is valid for people working in the software industries as well. Therefore, when assigning the most suitable tasks to people according to their personality type, a software development company’s succession rate can be proliferated to a remarkable level. In this manner, the main theme of this empirical research is to find relationships that establish links between personality type and their job designation preferences in the software industry. For this purpose, this study is comprised of 44 Pakistan developers, who are working in different software houses and are directly involved in developing software projects. In addition, an MBTI (Myers-Briggs Type Indicator) test indicator is used for the link establishment. With respect to the reported results, tester, team lead, and project manager are found to be ENFJs, which is the least common type in software developers. However, for web developers and software engineers, ISFJ is found to be the most preferable type, with an edge over ENFJ.