Sentiment Analysis on Twitter for the Major German Parties during the 2021 German Federal Election

We present the results of a project performing sentiment analysis on tweets from German politicians and party accounts for the 2021 German federal election. We collected over 58,000 tweets from the Twitter accounts of the seven parties represented in the German Bundestag, of which a selection of 2,000 tweets were annotated by three annotators. Based on the annotated data, we implemented multiple sentiment analysis approaches and evaluated the sentiment classification performance. We found that transformer-based models like bidirectional encoder from transformers (BERT) performed better than traditional machine learning models such as Naive Bayes and lexiconbased models like GerVADER. The best performing BERT model achieved an accuracy of 93.3% and macro f1 score of 93.4%. Applying sentiment analysis on the overall corpus via this method showed that overall, negative sentiment was most frequent and that there were multiple major shifts in sentiment a few months before and after the election. Furthermore, we found that tweets from opposition parties had on average more negative sentiment than those from governing parties

How Sociotechnical Realignment and Sentiments Concerning Remote Work are Related – Insights from the COVID-19 Pandemic

The COVID-19 pandemic forced sociotechnical systems (STS) to highly integrate remote work. Large-scale analyses show that the positivity of tweets about work from home decreased until COVID-19 was declared a pandemic by the WHO and re-increased in the weeks that followed. Nevertheless, it is unclear if this reaction is due to personal and organizational developments or if it mirrors the realignment of entire STS. The present study uses Q methodology to identify differences in how STS realign to the externally enforced integration of remote work. Only STS that reach a state of high alignment to remote work conditions by successfully shifting communication and procedures to digital spheres can be considered resilient. The results show that employees describe their personal experiences with remote work as more positive the higher their level of sociotechnical realignment. Furthermore, personal digital resilience is correlated to successful STS realignment as well. The results confirm the importance of realigning not only the technical and social components of STS but above all their sociotechnical interaction. Negative sentiments relate in particular to the low realization of humanistic objectives in STS