Text processing

As explained in Graph Algorithms for Data Science

Text processing is the computational manipulation of textual data through operations such as tokenization, replacement, and filtering.

How Graph Algorithms for Data Science covers it

How deep it goes

Demonstrates executable Cypher steps for cleaning, tokenizing, filtering, normalizing, and storing hashtags, without deeper NLP methods.

Examples

Uses tweets, hashtags, extracted-hashtag tables, Cypher listings, and UNWIND output rows as concrete artifacts.

What it assumes

Assumes tweet data and basic Cypher familiarity while teaching replace, split, filtering, list comprehension, and UNWIND operations.

What you can do after

Apply it via Cypher to extract normalized hashtags and create Tag nodes linked to their source tweets.

How it connects

Connects tokenization with natural language processing, hashtag extraction with co-occurrence networks, and list processing with graph storage.

liveBookmanning.com

Read Graph Algorithms for Data Science on liveBook Get the book at manning.com

Related concepts

• clojure.stringaddressed by · clojure.string is the Clojure standard-library namespace providing additional string-processing functions.
• regular expressionused by · A formal pattern notation that describes sets of strings and enables text to be matched, searched, extracted, or transformed.
• signalproduces · A signal is an observable value, event, or pattern that conveys information about a condition, state, or phenomenon.
• splitused by · Split is an operation or function that divides a string or sequence into parts according to a separator, pattern, or other rule.
• concatenationused by · Concatenation is the operation of joining ordered sequences end to end to form a single sequence.

This explainer was generated by Manning's knowledge graph from the text of Graph Algorithms for Data Science. The section links point to the parts of the book it draws on.