Corporate reputation can be defined as the overall assessment of a company's performance over time (Kircova & Esen, 2018).Organizations with a positive corporate reputation create a competitive advantage and are more likely to influence customer's behaviors and attitudes (Kircova, 2018).Measuring corporate reputation is an increasingly important area in business studies because the amount of opinions and comments is increasingly growing on the internet and has become very accessible to strangers (Shayaa, 2018).Traditionally, corporate reputation is measured with well-known approaches such as surveys, qualitative interviews, and sample groups (Smith, 2010).Researchers like Fombrun & Fonzy and Newburry have already drawn attention to a new method of measuring corporate reputation.In Fombrun's most recent research, an instrument was developed to measure corporate reputation and develop predictive models of reputation's impact on stakeholder outcomes (Fombrun, Fonzy, Newburry, 2015).So far, however, there has been little discussion about measuring corporate reputation with sentiment analysis using online reviews.This paper applies sentiment analysis as a suitable method to explore how employees perceive organizations, hence measuring corporate reputation.By using our tool, organizations can adapt to market changes and cater to stakeholders' needs.Also, it can be used to raise awareness for organizations that are unaware of negative reviews online.

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%.

Companies use crowdsourcing to solve specific problems or to search for innovation. By using open innovation platforms, where community members propose ideas, companies can better serve customer needs. So far, it remains unclear which factors influence idea implementation in crowd sourcing context. With the research idea that we present here, we aim to get a better understanding of the success and failure of ideas by examining relationships between characteristics of ideators, characteristics of ideas and the likelihood of implementation. In order to test the methodological approach that we propose in this paper in which we investigate for business relevant innovativeness as well as sentiment based on text analytics, data including unstructured text was mined from Dell IdeaStorm using webcrawling and scraping techniques. Some relevant hypotheses that we define in this paper were confirmed on the Dell IdeaStorm dataset but in order to generalize our findings we want to apply to the Leg o dataset in our current work in progress. Possible implications of our novel research idea can be used to fill theoretical gaps in marketing literature, help companies to better structure their search for innovation and for ideators to better understand factors contributing to successful idea generation.