Participants will be introduced to the fundamentals of text content analysis, focusing on techniques to preprocess and analyze textual data. The session will cover text cleaning, tokenization, stemming, and lemmatization using libraries such as Gensim and Pandas. Attendees will learn how to convert raw text into structured data, suitable for further analysis. We will continue with an introduction to text feature extraction methods, including Bag-of-Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF). Next we will delve into advanced discourse analysis techniques and machine learning applications, utilizing Scikit-learn and Gensim. Participants will explore various algorithms for topic modeling, such as Latent Dirichlet Allocation (LDA), and sentiment analysis, including Naïve Bayes and Support Vector Machines (SVM). Attendees will learn how to evaluate and fine-tune their models for optimal performance. The workshop will culminate in the creation of interactive visualizations using the Plotly library, empowering participants to effectively communicate their findings and insights to a broader audience.