Scraping data on TikTok that includes the hashtag #feminism is important for sociologists for several reasons. First, it allows us to understand the perspectives and experiences of individuals who engage with feminism on this popular social media platform. Second, analyzing the data can provide insights into how feminist ideas and activism are communicated and shared in a visually-driven and interactive format. Finally, examining the hashtag #feminism on TikTok can contribute to a broader understanding of the role of digital media in shaping feminist discourse and mobilization among younger generations.

In this report we made some basic visualization using the different variables in the world bank dataset which explores the relationship between people's socio-economics status and the life expectancy.

In this project, we analysed and predicted the sentiment of covid-19 related tweets based on the words they contain. The prediction models used in this project are Naive Bayes and Random Forest classification models. In addition, we proposed an exploratory analysis of the tweet data for a better understanding of the project. The project is an assignment in the Computational Methods for Text Analysis course at the Higher School of Economics SPB.