Econometrics meets sentiment : an overview of methodology and applications

The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software

Sentiment Analysis using an ensemble of Feature Selection Algorithms

To determine the opinion of any person experiencing any services or buying any product, the usage of Sentiment Analysis, a continuous research in the field of text mining, is a common practice. It is a process of using computation to identify and categorize opinions expressed in a piece of text. Individuals post their opinion via reviews, tweets, comments or discussions which is our unstructured information. Sentiment analysis gives a general conclusion of audits which benefit clients, individuals or organizations for decision making. The primary point of this paper is to perform an ensemble approach on feature reduction methods identified with natural language processing and performing the analysis based on the results. An ensemble approach is a process of combining two or more methodologies. The feature reduction methods used are Principal Component Analysis (PCA) for feature extraction and Pearson Chi squared statistical test for feature selection. The fundamental commitment of this paper is to experiment whether combined use of cautious feature determination and existing classification methodologies can yield better accuracy

Analysis of Human Affect and Bug Patterns to Improve Software Quality and Security

The impact of software is ever increasing as more and more systems are being software operated. Despite the usefulness of software, many instances software failures have been causing tremendous losses in lives and dollars. Software failures take place because of bugs (i.e., faults) in the software systems. These bugs cause the program to malfunction or crash and expose security vulnerabilities exploitable by malicious hackers. Studies confirm that software defects and vulnerabilities appear in source code largely due to the human mistakes and errors of the developers. Human performance is impacted by the underlying development process and human affects, such as sentiment and emotion. This thesis examines these human affects of software developers, which have drawn recent interests in the community. For capturing developers’ sentimental and emotional states, we have developed several software tools (i.e., SentiStrength-SE, DEVA, and MarValous). These are novel tools facilitating automatic detection of sentiments and emotions from the software engineering textual artifacts. Using such an automated tool, the developers’ sentimental variations are studied with respect to the underlying development tasks (e.g., bug-fixing, bug-introducing), development periods (i.e., days and times), team sizes and project sizes. We expose opportunities for exploiting developers’ sentiments for higher productivity and improved software quality. While developers’ sentiments and emotions can be leveraged for proactive and active safeguard in identifying and minimizing software bugs, this dissertation also includes in-depth studies of the relationship among various bug patterns, such as software defects, security vulnerabilities, and code smells to find actionable insights in minimizing software bugs and improving software quality and security. Bug patterns are exposed through mining software repositories and bug databases. These bug patterns are crucial in localizing bugs and security vulnerabilities in software codebase for fixing them, predicting portions of software susceptible to failure or exploitation by hackers, devising techniques for automated program repair, and avoiding code constructs and coding idioms that are bug-prone. The software tools produced from this thesis are empirically evaluated using standard measurement metrics (e.g., precision, recall). The findings of all the studies are validated with appropriate tests for statistical significance. Finally, based on our experience and in-depth analysis of the present state of the art, we expose avenues for further research and development towards a holistic approach for developing improved and secure software systems