Vector Embedding & Sentiment Analysis

6/22/20261 min read

Cosine similarity measures how similar two vectors are by looking at the angle between them rather than their magnitudes. It's the cosine of that angle, computed as the dot product of the two vectors divided by the product of their lengths:

cos(θ) = (A · B) / (‖A‖ ‖B‖)

Values range from -1 to 1: 1 means the vectors point in the same direction (maximally similar), 0 means they're orthogonal (unrelated), and -1 means they point in opposite directions. Because it ignores magnitude and only considers orientation, it's widely used in text analysis, embeddings, and recommendation systems where the direction of a vector (e.g., word frequency patterns) matters more than its scale.

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