Pustulka, Elzbieta
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Sentiment analysis for a swiss gig platform company
We work with a Swiss Gig Platform Company to identify innovative solutions which could strengthen its position as a market leader in Switzerland and Europe. The company mediates between employers and employees in short term work contracts via a platform system. We first looked at the business processes and saw that some process parts were not being controlled by the company, which is now being remedied. Second, we analyzed the job reviews which the employers and employees write, and implemented a prototype which can detect negative statements automatically, even if the review is positive overall. We worked with a dataset of 963 job reviews from employers and employees, in German, French and English. The reviews have a star rating (1 to 4 stars), with some discrepancies between the star rating and the text. We scored the reviews manually as negative or other, as negative reviews are important for business improvement. We tested several machine learning methods and a hybrid method from Lexalytics.
Multilingual Sentiment Analysis for a Swiss Gig
We are developing a multilingual sentiment analysis solution for a Swiss human resource company working in the gig sector. To examine the feasibility of using machine learning in this context, we carried out three sentiment assignment experiments. As test data we use 963 hand annotated comments made by workers and their employers. Our baseline, machine learning (ML) on Twitter, had an accuracy of 0.77 with the Matthews correlation coefficient (MCC) of 0.32. A hybrid solution, Semantria from Lexalytics, had an accuracy of 0.8 with MCC of 0.42, while a tenfold cross-validation on the gig data yielded the accuracy of 0.87, F1 score 0.91, and MCC 0.65. Our solution did not require language assignment or stemming and used standard ML software. This shows that with more training data and some feature engineering, an industrial strength solution to this problem should be possible.
A game teaching population based optimization using teaching-learning-based optimization
We want to lower the entry barrier to optimization courses. To that aim, we deployed a game prototype and tested it with students who had no previous optimization experience. We found out that the prototype led to an increased student motivation, an intuitive understanding of the principles of optimization, and a strong interaction in a team. We will build on this experience to develop further games for classroom use.
Identifikation von Meinungsrobotern
Der Bericht des Bundesrats «Rechtliche Basis für Social Media» (Bundesrat 2017) stellt fest, dass soziale Medien bei der öffentlichen Meinungsbildung an Bedeutung gewinnen und parallel dazu eine «zunehmende Beeinflussung bzw. Manipulation des politischen Diskurses» stattfindet. Gemeint sind insbesondere Meinungsroboter (als Vertreter der Social Bots), welche durch maschinelle Kommunikation die Themenagenda beeinflussen, Diskussionsgruppen infiltrieren und Nutzerprofile sabotieren. Die Hasler-Stiftung wurde zum Jahreswechsel 2017/18 darum gebeten, das Projekt «Identifikation von Meinungsrobotern» zu unterstützen. Informations- und Meinungsroboter werden, so die Argumentation ihr gegenüber, die Informationsflüsse einer Gesellschaft nachhaltig beeinflussen und verändern – nicht nur in der Politik. Ihre Aktivitäten zu erkennen, zu verstehen und zu bewerten gilt als notwendige Voraussetzung, um ihre Spielräume zu definieren und zu begrenzen.
An experiment with an optimization game
We aim to improve the teaching of the principles of optimization, including computational intelligence (CI), to a mixed audience of business and computer science students. Our students do not always have sufficient programming or mathematics experience and may be put off by the expected difficulty of the course. In this context we are testing the potential of games in teaching. We deployed a game prototype (design probe) and found out that the prototype led to increased student motivation, intuitive understanding of the principles of optimization, and strong interaction in a team. Ultimately, with the future work we sketch out, this novel approach could improve the learning and understanding of optimization algorithms and CI in general, contributing to the future of Explainable AI (XAI).
Gig work business process improvement
We collaborate with a gig work platform company (GPC) in Switzerland. The project aims to improve the business by influencing process management within the GPC, providing automated matching of jobs to workers, improving worker acquisition and worker commitment, and particularly focusing on the prevention of no shows. One expects to achieve financial, organizational and efficiency gains. As research tools we use a combination of text mining and sentiment analysis, Business Process Modeling and Notation (BPMN), interviews with workers and employers, and the design of sociotechnical improvements to the process, including platform improvements and prototypes. Here, we focus on the successful combination of BPMN modelling with sentiment analysis in the identification of problems and generation of ideas for future modifications to the business processes.