Neural sentiment analysis of user reviews to predict user ratings

This is an accepted manuscript of an article published by IEEE in 2019 4th International Conference on Computer Science and Engineering (UBMK) on 21/11/2019, available online: https://ieeexplore.ieee.org/document/8907234 The accepted version of the publication may differ from the final published version.The significance of user satisfaction is increasing in the competitive open source software (OSS) market. Application stores let users send their feedbacks for applications, which are in the form of user reviews or ratings. Developers are informed about bugs or any additional requirements with the help of this feedback and use it to increase the quality of the software. Moreover, potential users rely on this information as a success indicator to decide downloading the applications. Since it is usually costly to read all the reviews and evaluate their content, the ratings are taken as the base for the assessment. This makes the consistency of the contents with the ratings of the reviews important for healthy evaluation of the applications. In this study, we use recurrent neural networks to analyze the reviews automatically, and thereby predict the user ratings based on the reviews. We apply transfer learning from a huge volume, gold dataset of Amazon Customer Reviews. We evaluate the performance of our model on three mobile OSS applications in the Google Play Store and compare the predicted ratings and the original ratings of the users. Eventually, the predicted ratings have an accuracy of 87.61% compared to the original ratings of the users, which seems promising to obtain the ratings from the reviews especially if the former is absent or its consistency with the reviews is weak.Published versio

Affective Trust As a Predictor of Successful Collaboration in Distributed Software Projects

Building trust among remote developers is challenging because trust typically grows through close face-to-face interaction. In this paper, we present the preparatory design of an empirical study aimed to assess whether affective trust, established through social communication between developers, is a predictor of successful collaboration in distributed projects. Specifically, we intend to measure affective trust through sentiment analysis of pull-request comments

None of Your Beeswax: The Role of Perceived Coworker Nosiness and Interpersonal Trust in Predicting Knowledge Provision at Work

As group- and team-based employment structures increase in popularity, it is important to understand the factors that promote or inhibit the transfer of knowledge or information between employees. Given that knowledge transfer processes often occur as a result of requests for knowledge or information from information targets by information seekers, this dissertation focused on a specific form of information-seeking behaviors – coworker nosiness – and the process through which perceptions of coworker nosiness result in knowledge sharing and knowledge hiding behaviors. Perceived coworker nosiness refers to behaviors judged by information targets as high-frequency information-seeking behaviors that are meant to gather information that is overly personal in nature and/or irrelevant to information seekers\u27 abilities to carry out their jobs effectively. Although affective trust was hypothesized to mediate relationships between coworker nosiness and both knowledge sharing and knowledge hiding, results across two studies – one using an experimental methodology and the other using a time-lagged survey design – found that higher levels of cognitive trust felt toward information targets rather than affective trust resulted in more knowledge sharing and less knowledge hiding. Additional analyses were conducted to consider alternative explanations and examine relationships with other relevant constructs. Discussions of the strengths and limitations of both studies as well as the practical implications and future research directions are provided