There’s probably a nice shell multiline command that does what you want lol. cat + awk unique count + sort
I’m just forgetting is there’s an easy way to keep the line numbers or filename so you can easily go back to the full page reference.
Comment on This Tool Searches the Epstein Files For Your LinkedIn Contacts
Sterile_Technique@lemmy.world 1 day ago
Is there a tool that crunches the entirety of the documents and sorts the individual words by frequency? For example, doing it the stupid way (semi-manually) I copied OP’s article into Word and replaced every space with a page break to turn the entire article into a one-word-per-line list, then plugged that into Excel and sorted alphabetically, then manually counted and deleted the repeats. Then sorted those to put the most frequent on top.
This reduced the 525 word article down to a list of 284 individual words. If I added another article to this list, the number of entries would only be increased by the number of words in the 2nd article that didn’t appear in the first one, so basically as more and more articles are added, the number of unique additions from each would be fewer and fewer. Do this to a thousands-of-pages of documents like the Epstein files, and you could instantly condense like dozens of pages worth of just the word “the” down to a single entry, making the entirety of the documents much easier to skim for highlights… like, if the word ‘velociraptor’ was just randomly hidden in the article, most readers would probably skim right passed it; but in the list below it would stand out like a sore thumb, prompting a targeted search in the full document for context. Especially if we could flag words as not interesting, and like click to knock “the” “of” “and” etc off the list.
…maybe a project for someone who actually knows what they’re doing… my skills hit a brick wall after things like ‘find and replace’ in Word, but you get the gist.
| Word used: | # found: |
|---|---|
| The | 37 |
| Of | 16 |
| And | 14 |
| To | 14 |
| Epstein | 11 |
| In | 11 |
| Tool | 9 |
| A | 8 |
| I | 8 |
| Files | 7 |
| But | 5 |
| For | 5 |
| Is | 5 |
| 5 | |
| Many | 5 |
| On | 5 |
| That | 5 |
| With | 5 |
| 404 | 4 |
| Also | 4 |
| An | 4 |
| Connections | 4 |
| Found | 4 |
| Media | 4 |
| Not | 4 |
| People | 4 |
| All | 3 |
| Anything | 3 |
| Are | 3 |
| As | 3 |
| Him | 3 |
| It | 3 |
| My | 3 |
| Network | 3 |
| Them | 3 |
| Were | 3 |
| Who | 3 |
| Already | 2 |
| Appears | 2 |
| Case | 2 |
| Common | 2 |
| Con | 2 |
| Def | 2 |
| Documents | 2 |
| DOJ | 2 |
| Dump | 2 |
| Each | 2 |
| Excerpts | 2 |
| Find | 2 |
| Finke | 2 |
| Founder | 2 |
| From | 2 |
| How | 2 |
| Jeffrey | 2 |
| Me | 2 |
| Mentioned | 2 |
| Moss | 2 |
| Name | 2 |
| Names | 2 |
| Obviously | 2 |
| Other | 2 |
| Overlap | 2 |
| Page | 2 |
| Positives | 2 |
| Repository | 2 |
| Said | 2 |
| Search | 2 |
| Their | 2 |
| This | 2 |
| Up | 2 |
| Vincenzo | 2 |
| Work | 2 |
| Your | 2 |
| 5 | 1 |
| 22 | 1 |
| 35 | 1 |
| 1st | 1 |
| 2nd | 1 |
| 3rd | 1 |
| Acknowledges | 1 |
| Across | 1 |
| Adam | 1 |
| Added | 1 |
| After | 1 |
| Although | 1 |
| Anyone | 1 |
| Api | 1 |
| Appearance | 1 |
| Approached | 1 |
| Attended | 1 |
| Audio | 1 |
| Away | 1 |
| Badges | 1 |
| Based | 1 |
| Be | 1 |
| Because | 1 |
| Behind | 1 |
| Between | 1 |
| Brin | 1 |
| Built | 1 |
| Called | 1 |
| Can | 1 |
| Chose | 1 |
| Christopher | 1 |
| Company | 1 |
| Conference | 1 |
| Contained | 1 |
| Contains | 1 |
| Context | 1 |
| Could | 1 |
| Couldn’t | 1 |
| Court | 1 |
| Covered | 1 |
| Co-Worker | 1 |
| Creator | 1 |
| Days | 1 |
| Deep | 1 |
| Degree | 1 |
| Department | 1 |
| Deranged | 1 |
| Did | 1 |
| Didn’t | 1 |
| Do | 1 |
| Document | 1 |
| Does | 1 |
| Don’t | 1 |
| Down | 1 |
| Duggan | 1 |
| Easily | 1 |
| Elites | 1 |
| 1 | |
| Epstein’s | 1 |
| Far | 1 |
| First | 1 |
| Free | 1 |
| Fully | 1 |
| Ghislaine | 1 |
| Girls | 1 |
| Github | 1 |
| Gut | 1 |
| Hacker | 1 |
| Hacking | 1 |
| Had | 1 |
| Have | 1 |
| He | 1 |
| His | 1 |
| Hits | 1 |
| Images | 1 |
| Incidental | 1 |
| Included | 1 |
| Inclusion | 1 |
| Initial | 1 |
| Introduce | 1 |
| Investigations | 1 |
| Involvement | 1 |
| Iozzo | 1 |
| Jeff | 1 |
| Just | 1 |
| Justice’s | 1 |
| Keep | 1 |
| Know | 1 |
| Known | 1 |
| Larry | 1 |
| Last | 1 |
| Likely | 1 |
| Links | 1 |
| Lot | 1 |
| Made | 1 |
| Make | 1 |
| Mapped | 1 |
| Mash | 1 |
| Massive | 1 |
| Matching | 1 |
| Material | 1 |
| Maxwell | 1 |
| May | 1 |
| Mean | 1 |
| Mention | 1 |
| Mentions | 1 |
| Mentions | 1 |
| Mentions | 1 |
| Million | 1 |
| Moss’s | 1 |
| Multiple | 1 |
| Musk’s | 1 |
| Myself | 1 |
| Necessarily | 1 |
| Nefarious | 1 |
| Never | 1 |
| New | 1 |
| No | 1 |
| Nude | 1 |
| Number | 1 |
| Off | 1 |
| Offered | 1 |
| Only | 1 |
| Or | 1 |
| Original | 1 |
| Others | 1 |
| Output | 1 |
| Pages | 1 |
| Paid | 1 |
| Patrick | 1 |
| Peter | 1 |
| Photos | 1 |
| Pointed | 1 |
| Position | 1 |
| Post | 1 |
| Previous | 1 |
| Produce | 1 |
| Programmers | 1 |
| Publicly | 1 |
| Published | 1 |
| Purposefully | 1 |
| Reads | 1 |
| Realize | 1 |
| Recordings | 1 |
| 1 | |
| Related | 1 |
| Released | 1 |
| Relevance | 1 |
| Report | 1 |
| Reported | 1 |
| Result’s | 1 |
| Review | 1 |
| S | 1 |
| Saw | 1 |
| Scenes | 1 |
| Searched | 1 |
| Searches | 1 |
| Sergey | 1 |
| Show | 1 |
| Shows | 1 |
| Smarter | 1 |
| Social | 1 |
| Some | 1 |
| Stay | 1 |
| Stuff | 1 |
| Style | 1 |
| Suppose | 1 |
| Surprising | 1 |
| Taking | 1 |
| Tech | 1 |
| Tested | 1 |
| Than | 1 |
| Thankfully | 1 |
| There | 1 |
| These | 1 |
| Thiel | 1 |
| Those | 1 |
| Told | 1 |
| Tools | 1 |
| Total | 1 |
| Touch | 1 |
| Tried | 1 |
| Trusting | 1 |
| Understandably | 1 |
| Unredacted | 1 |
| Upload | 1 |
| Verify | 1 |
| Very | 1 |
| Videos | 1 |
| Visualize | 1 |
| Want | 1 |
| Warn | 1 |
| Way | 1 |
| We | 1 |
| Wealth | 1 |
| Website | 1 |
| Week | 1 |
| Well | 1 |
| Went | 1 |
| Where | 1 |
| Whether | 1 |
| Wikipedia | 1 |
| Wild | 1 |
| Wired | 1 |
| Women | 1 |
| Wondering | 1 |
| Would | 1 |
| Wrote | 1 |
| You | 1 |
| Zero | 1 |
mlg@lemmy.world 1 day ago
thanks_shakey_snake@lemmy.ca 1 day ago
Seriously, if you’re motivated enough to do this, you should give programming a try. Python or Ruby or Javascript are ideal for this kind of thing, and you can solve problems like this in a few lines of code… just look up “word frequency in Python” or whatever language for examples.
If you want to see what the next level of this kind of analysis looks like, watch a few videos about how Elasticsearch works… not so much so you can USE Elasticsearch (although you can, it’s free), but just to get a sense of how they approach problems like this: Like imagine instead of just counting word occurrences, you kept track of WHERE in the text the word was. You could still count the number of occurrences, but also find surrounding text and do a bunch of other interesting things too.
Sterile_Technique@lemmy.world 17 hours ago
I kind of dove a bit deeper into that kind of shenanigans for nursing school - copying entire chapters from my text books and using find and replace wildcards clip out all the bullshit like in-text citations.
Plug that fucker into some text-to-speech software, and my reading assignment just became a listening assignment!
I do wish I knew some actual code… I’ve tried to dive in and self-learn that stuff, but didn’t make it very far.
I should take an actual class.
zarkanian@sh.itjust.works 12 hours ago
There’s freeCodeCamp which is (wait for it)…free.