Term-Document Matrix Construction:

• Generation: Create a term-document matrix from a collection of text documents. Each row represents a term, and each column represents a document. The entries in the matrix capture the frequency of terms within each document.

• Preprocessing: Apply tokenization, stemming, and removal of stop words to prepare terms. This preprocessing ensures that terms are standardized for accurate analysis.

Query Handling and Vectorization

• Query Input: Receive a text query from the user, which is preprocessed in the same manner as the documents to ensure consistency.
• Query Vector Construction: Convert the preprocessed query into a query vector. This vector aligns with the dimensions of the term-document matrix, enabling direct comparison with document vectors.

Similarity Measurement

Cosine Similarity: Calculate the cosine similarity between the query vector and each document vector in the term-document matrix. Cosine similarity measures the cosine of the angle between two vectors, providing a metric for their similarity.

Formula: The cosine similarity between vectors A and B is given by:

Implementation:Compute the dot product of the query vector and document vectors, and divide by the product of their magnitudes. This results in a similarity score between 0 and 1.

Ranking and Output

• Document Ranking: Rank the documents based on their similarity scores to the query vector. Higher scores indicate greater relevance to the query.

• Output Format: Present the ranked documents along with their similarity scores. The output is designed to be processed by a colleague's web interface, ensuring seamless integration and display.