Behind the speeches: A topic modeling analysis of colombian presidential candidates (2026)

1. 1Collect a dataset of speeches from Colombian presidential candidates
2. 2Preprocess the text data by tokenizing and removing stop words
3. 3Apply topic modeling techniques, such as Latent Dirichlet Allocation (LDA), to identify hidden themes
4. 4Visualize the results using dimensionality reduction techniques, such as t-SNE or PCA
5. 5Compare the topic models to identify similarities and differences between the candidates' speeches

Data scientists and machine learning engineers can benefit from this approach to analyze large datasets of text, such as political speeches, to identify patterns and themes. This can be useful for researchers, policymakers, and journalists to gain insights into the topics and ideas discussed by politicians.

💡 Topic modeling can be used to analyze large datasets of text and identify patterns and themes, without requiring manual labeling or annotation

Uncover hidden themes in political speeches with topic modeling!