Cosine Similarity and Document Analysis

The video tutorial introduces cosine similarity and cosine distance, explaining their application in data science. It uses a practical example involving financial documents to illustrate how word count ratios can help identify document topics. The tutorial discusses the challenges of real-world document analysis and demonstrates how vector mathematics can be used to determine document similarity. It explains cosine similarity and distance with examples and concludes with a Python implementation of these concepts.

What is the primary focus of cosine similarity in data science?

• To measure the distance between two points

• To calculate the angle between two vectors

• To determine the similarity between two documents

• To find the magnitude of a vector

In the example given, what indicates that a document is likely about Apple?

• The document's title

• The number of pages in the document

• The ratio of 'iPhone' to 'Galaxy' mentions

• The presence of the word 'Samsung'

What complicates the real-world document classification process?

• The need for manual annotation

• The presence of multiple languages

• The mention of multiple competitors

• The length of the document

How is document similarity determined using vectors?

• By counting the number of words in each document

• By analyzing the document's metadata

• By calculating the angle between the vectors

• By comparing the lengths of the vectors

What is the cosine similarity between two identical vectors?

• 1

• 0

• -1

• 0.5

What does a cosine distance of zero indicate?

• The vectors are unrelated

• The vectors are opposite

• The vectors are identical

• The vectors are perpendicular

Which Python module is used for calculating cosine similarity?

• Sklearn

• NumPy

• Pandas

• Matplotlib

What is the purpose of creating a Pandas DataFrame in this context?

• To visualize data

• To perform mathematical operations

• To analyze document word counts

• To store document metadata

What is the cosine similarity between documents with vectors (3,1) and (3,2)?

• 0.5

• 1

• 0.75

• 0.96