Jeff James jrjames83

Me: is tf-idf sensitive to word order?

ChatGPT: TF-IDF (term frequency-inverse document frequency) is a numerical statistic that is used to reflect the importance of a word in a document or a collection of documents. It is commonly used in text analysis and information retrieval to determine the relevance of a document to a particular query, and to determine the similarity between documents.

TF-IDF is based on the frequency of words within a document, and is therefore sensitive to variations in word order. This means that the TF-IDF score for a word can be different depending on where the word appears in the document, and whether it is next to other words with high or low TF-IDF scores.

For example, consider the following two sentences:

	4 qid:1 # 7555 rambo
	3 qid:1 # 1370 rambo
	3 qid:1 # 1369 rambo
	2 qid:1 # 13258 rambo
	4 qid:1 # 1368 rambo
	1 qid:1 # 31362 rambo
	1 qid:1 # 61410 rambo
	0 qid:1 # 319074 rambo
	0 qid:1 # 10296 rambo
	0 qid:1 # 35868 rambo

	import requests
	import pandas as pd
	from bs4 import BeautifulSoup
	from datetime import datetime
	import logging

	headers = {
	"authority": "www.amazon.com",
	"pragma": "no-cache",
	"cache-control": "no-cache",

	import string

	# Some random document
	document = """BigQuery sure makes life easier for data scientists. You can query data for insights, build high quality ML models and easily interface with other Google Cloud services."""

	# Remove punctuation
	doc_wo_punct = document.translate(str.maketrans('', '', string.punctuation))

	# Some keywords we'd like to extract
	keywords = ["bigquery", "ML", "insights", "SQL", "analysis"]

	-- WITH keyphrases AS (

	-- SELECT '\\b' \|\| name \|\| '\\b' as keyword
	-- FROM `bigquery-public-data.usa_names.usa_1910_2013`

	-- ), docs AS (

	-- SELECT title as doc
	-- FROM `bigquery-samples.reddit.full`