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The earnest platform

Increasingly, Instagram is discussed as a site for misinformation, inau-thentic activities, and polarization, particularly in recent studies aboutelections, the COVID-19 pandemic and vaccines. In this study, we havefound a different platform. By looking at the content that receives themost interactions over two time periods (in 2020) related to three U.S.presidential candidates and the issues of COVID-19, healthcare, 5G andgun control, we characterize Instagram as a site of earnest (as opposedto ambivalent) political campaigning and moral support, with a rela-tive absence of polarizing content (particularly from influencers) andlittle to no misinformation and artificial amplification practices. Mostimportantly, while misinformation and polarization might be spreadingon the platform, they do not receive much user interaction.

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%.

Exploring Corporate Reputation based on Sentiment Polarities That Are Related to Opinions in Dutch Online Reviews

This research demonstrates the power and robustness of the vocabulary method by Hernández-Rubio et al. (2019) for aspect extraction from online review data. We showcase that this algorithm not only works on the English language based on the CoreNLP toolkit, but also extend it on the Dutch language, specifically with aid of the Frog toolkit. Results on sampled datasets for three different retailers show that it can be used to extract fine-grained aspects that are relevant to acquire corporate reputation insights.

Trends in distribution centres and their locations

Withered mainstream areas and polarization

In this article, it is argued that In polarization, we fixate on two poles, like true and false, news versus fake news, real versusfalse, or in a social context, rich versus poor, and good versus bad. This is an instance of singular thinking, because a third factor is overlooked, being the area between the two poles, where the polarized elements come from. The gap, divide or rift, is in fact an area. With growth at the poles, polarization, instead of fixating on the poles, one should also look at the intermediate area, the mainstream area.

Vallende ster

De toekomst van de boekhandel in het algemeen, en Polare in het bijzonder is onzeker.