Using Keyword Frequency Without Pretending It Reveals Personality

Counting words is the easiest analysis to run on a text corpus and the easiest to over-read. The count itself is arithmetic and hard to get wrong. What people do with it afterwards is where the trouble starts.

What a word count actually is

Frequency analysis tells you which tokens appear most often in text you loaded, after removing tokens you decided did not count.

Every part of that sentence carries a caveat.

In text you loaded. Not everything the account wrote — the records the archive held and you retrieved. Different limits produce different top terms.

Tokens. Whatever your tokenizer decided was a word. Whether "don't" is one token or two, whether URLs are stripped, whether "typescript" and "TypeScript" merge — these are choices, and they change the output.

You decided did not count. Stopword lists are editorial. Removing "just" is defensible; it is also removing a word that carries hedging. Nobody agrees on the boundary between function words and content words, so every list encodes a position.

None of this makes frequency analysis useless. It makes it a description of a corpus under stated processing choices, which is a real thing to have.

Where it goes wrong

The failure is not miscounting. It is the leap from this word appears often to this person is like that.

Consider an account whose frequent terms include "kubernetes," "latency," and "deploy." The safe reading: this text discusses infrastructure. The unsafe reading: this person is a backend engineer.

The second might be right. It also might be a technical writer, a student, a manager repeating vocabulary from meetings, or someone who spent one month arguing about it and never mentioned it again. Frequency does not distinguish these, because it does not record why a word appeared.

It gets worse with terms that carry social weight. A word appearing frequently in someone's comments could reflect their view, a topic they argue against, something they quote, or a community's vocabulary they adopted. Aggregation flattens all four into one number.

Things a word count cannot see

Negation. "I don't think X" and "X is right" both count X.

Sarcasm. Reddit runs on it, and it inverts meaning invisibly.

Quotation. Replies quote what they respond to, so the quoted text becomes the replier's word count.

Recency. A term used heavily in 2019 and never since ranks alongside one used every week.

Context. A word in a technical thread and the same word in an argument count identically.

Making it useful

Compare within, not across. Comparing a corpus to itself over time — did this vocabulary rise or fall — is more defensible than comparing two people's word lists, because the processing choices are held constant.

Read the excerpts. The number tells you where to look. What a term means in context requires reading the matches, which is why a frequency tool that cannot show you the surrounding text is only doing half the job.

Search rather than browse. Asking "how often does this corpus use X" is a question. Reading a top-fifty list and pattern-matching is not — it invites you to construct a narrative from whatever surfaced.

Watch for community vocabulary. Someone active in a niche community will show that community's jargon. That is a fact about where they posted, not about them.

What honest tooling looks like

A frequency table with a count and nothing else invites interpretation without giving you the means to check it. Better tooling:

• says which records the count covers, and that changing the limit changes the answer
• shows matching excerpts alongside every figure
• matches on word boundaries, so a search for one term does not silently include longer words containing it
• keeps exact-phrase search available, since phrases survive aggregation better than words
• excludes archive markers like [deleted], which are metadata rather than anything a person wrote

And it should say plainly what the numbers do not describe. Word frequency describes loaded text, not a person's identity or beliefs. That sentence is not decoration. It is the difference between a description and an accusation.