Demystifying Cosine Similarity

Cosine similarity is a metric used in Natural Language Processing (NLP) to measure the semantic similarity between documents or words. The metric is based on the cosine of the angle between two vectors, where a value of 1 indicates the vectors point in the same direction (high similarity), -1 indicates opposite directions (antonyms), and 0 indicates orthogonality (no relation). Unlike Euclidean distance, cosine similarity is not affected by the magnitude of the vectors, making it ideal for comparing texts of different lengths where directional similarity is more important. The article provides Python code examples to calculate the metric and demonstrates how different embedding models can represent semantic overlap and polarity.