Feature extraction is the process of converting complex data inputs, such as textual entities, into quantifiable numerical representations or vectors that machine learning models can process. This step is critical in the semantic analysis workflow as it transforms raw text into a format suitable for clustering algorithms. The predominant technique referenced for this task is TF-IDF (Term Frequency-Inverse Document Frequency).
When TF-IDF is applied, each entity name (e.g., from the entity column of a dataset) is transformed into a high-dimensional feature vector. This vectorization is achieved by weighting terms based on their frequency within the entity’s name relative to their inverse frequency across the entire dataset. The primary strategic benefit of using TF-IDF for feature extraction is that it captures the relevance of unique words while downweighting common, less meaningful terms (like “loan” or “credit”) in favor of specialized terms (like “crowdfunding”).
Once features are extracted and vectorized, they serve as the foundational input for subsequent steps in semantic analysis. For example, the TF-IDF vectors are clustered using methods like K-Means to group semantically similar entities, and then are subjected to Dimensionality Reduction techniques like PCA, which maps the high-dimensional vectors down to two dimensions (2D) for visual analysis while preserving the underlying semantic structure.