Topic Exploration

Here, we go further into the visualization of topic modeling using advanced techniques such as t-SNE and MDS.

What We Will Cover

• Two-dimensional visualization of documents: with t-SNE, we reduce the dimensionality of the corpus so that similar documents are positioned close to one another on a map. This helps explore the distribution of themes.
• Analysis of topic similarity with MDS: MDS (Multi-Dimensional Scaling) is used to visualize similarity between topics in a two-dimensional space. The closer the topic points are, the more similar their themes are. We also examine how frequent each topic is in the corpus.

Explaining t-SNE and MDS

• t-SNE (t-Distributed Stochastic Neighbor Embedding): a dimensionality-reduction technique that places similar documents near each other. It is particularly useful for complex data such as text because it preserves local relationships.
• MDS (Multi-Dimensional Scaling): a method for visualizing similarities between topics. It makes it possible to represent topic relationships on a map where distances between points reflect thematic dissimilarities.

These tools are important for understanding how topics are distributed and how they interact within a text corpus.

Note: in text analysis, the word “document” refers to a unit of textual analysis within a corpus. It may be a whole text or a segment of one. A corpus can contain several documents such as articles, tweets, or speeches.