Context Twitter is a good way to measure current reactions. And during the epidemic, Lockdown is frequently the subject of the platform. While almost every country in the world suffers heavy losses in this war, politicians are also exposed to harsh criticism. In this dataset, we would like to examine the comments on Twitter about German chancellor Angela Merkel, who ranks first in the list of the world's most powerful women by [Forbes](https://www.forbes.com/sites/forbespr/2020/12/08/angela-merkel-christine-lagarde-and-kamala-harris-top-forbes-100-most-powerful-women-list/?sh=38ad23931a5f). So we are curious about the results of the Lockdown arguments. Content The data was created in December-2020 as 1500 train and 650 test files about German chancellor Angela Merkel. Each tweet in the train data set has been labeled as positive or negative. Those behind the negative tweets were categorized under three headings. These are: - Conspiracy theory - Insult - Political criticism. Inspiration **Maybe you might below be wondering:** -In which language were the most positive or negative tweets? -What is the structure of the words used according to languages? -What are the reflections of the headings highlighted in negative comments according to languages? And while answering questions like this, you can find graphical options suitable for your exploratory data analysis. And a happy ending: You can develop a machine learning model for tweets that are not labeled in test data.