Zoekresultaten

A company’s corporate reputation through the eyes of employees measured with sentiment analysis of online reviews

A company’s corporate reputation through the eyes of employees measured with sentiment analysis of online reviews

Presence of Corporate Reputation Cues in Company Vacancy Texts Boosts Vacancy Attractiveness as Perceived by Employees

Attracting the best candidates online for job vacancies has become a challenging task for companies. One thing that could influence the attractiveness of organisations for employees is their reputation that is an essential component of marketing research and plays a crucial role in customer and employee acquisition and retention. Prior research has shown the importance for companies to improve their corporate reputation (CR) for its effect on attracting the best candidates for job vacancies. Company ratings and vacancy advertisements are nowadays a massive, rich valued, online data source for forming opinions regarding corporations. This study focuses on the effect of CR cues that are present in the description of online vacancies on vacancy attractiveness. Our findings show that departments that are responsible for writing vacancy descriptions are recommended to include the CR themes citizenship, leadership, innovation, and governance and to exclude performance. This will increase vacancies’ attractiveness which helps prevent labour shortage.

Sentiment polarity classification of corporate review data with a bidirectional Long-Short Term Memory (biLSTM) neural network architecture

A considerable amount of literature has been published on Corporate Reputation, Branding and Brand Image. These studies are extensive and focus particularly on questionnaires and statistical analysis. Although extensive research has been carried out, no single study was found which attempted to predict corporate reputation performance based on data collected from media sources. To perform this task, a biLSTM Neural Network extended with attention mechanism was utilized. The advantages of this architecture are that it obtains excellent performance for NLP tasks. The state-of-the-art designed model achieves highly competitive results, F1 scores around 72%, accuracy of 92% and loss around 20%.

Sentiment polarity classification of corporate review data with a bidirectional Long-Short Term Memory (biLSTM) neural network architecture

A considerable amount of literature has been published on Corporate Reputation, Branding and Brand Image. These studies are extensive and focus particularly on questionnaires and statistical analysis. Although extensive research has been carried out, no single study was found which attempted to predict corporate reputation performance based on data collected from media sources. To perform this task, a biLSTM Neural Network extended with attention mechanism was utilized. The advantages of this architecture are that it obtains excellent performance for NLP tasks. The state-of-the-art designed model achieves highly competitive results, F1 scores around 72%, accuracy of 92% and loss around 20%.

Leren van en over kwantificeren in de praktijk met Jack

Binnen de taalkunde is in de afgelopen decennia een schat aan inzichten opgedaan over de wijze waarop kinderen in de leeftijd van 4 tot 6 jaar betekenis toekennen aan zinnen met een kwantor. Helaas lijken deze resultaten niet of nauwelijks doorgedrongen te zijn in de psychologie, orthopedagogiek of praktijk van het primair onderwijs. De huidige ontwikkelingen op het gebied van sociale interactive agents (SIA’s) in combinatie met eerdere inzichten op het gebied van natural language processing (NLP), maakt dit wel mogelijk. Sterker nog, een ontwerpgerichte benadering van een dergelijke sociale interactive agent, die ik de naam Jack meegeef, vormt het ideale vertrekpunt voor verder interdisciplinair (praktijkgericht) onderzoek naar deze bouwstenen van ons logisch redeneervermogen.