Textual Data Analysis: Classification & Dimensionality Reduction

This lecture covers the fundamentals of textual data analysis, focusing on classification methods and dimensionality reduction techniques. It starts with an introduction to the objectives and frameworks of the analysis, delving into the classification complexity and methods such as Naive Bayes, logistic regression, and K-nearest neighbors. The lecture also explores dissimilarity matrices, usual metrics, and the evaluation of classification. Additionally, it discusses dimensionality reduction frameworks like Principal Component Analysis and Multidimensional Scaling, emphasizing the importance of choosing the right classification methods. The presentation concludes with key points on the optimization criteria for classification methods and the significance of visualization in understanding classification and clustering results.