WhatsApp Chat Analyzer
Analyse an exported WhatsApp chat in your browser: per-person message counts, hourly and weekday activity, emoji, media tallies and longest reply gaps.
Chat summary
Load or paste a WhatsApp export and the per-person breakdown, activity histograms and reply gaps appear here.
Your export is parsed entirely in your browser — the file is never uploaded, and closing the tab discards it. Timestamps are read exactly as written in the file and reported in that same clock, so no timezone conversion is applied and the numbers match what you see in the app. Exports in languages other than English may use different wording for media and deleted-message placeholders, in which case those two counts will read low.
What is the WhatsApp Chat Analyzer?
Export a chat from WhatsApp and this tool turns the resulting text file into statistics. It reports how many messages each participant sent and what share of the conversation that represents, along with word counts, average words per message, emoji use, media placeholders and deleted messages for each person.
- Reads both the iOS bracketed and Android dash export formats
- Per-participant messages, share, words, emoji, media and deleted counts
- Hour-of-day and day-of-week histograms with the busiest day flagged
- Longest waits for a reply, showing who was waiting for whom
- Automatic day/month versus month/day detection with a manual override
- The file is read in your browser and is never uploaded anywhere
How to use the WhatsApp Chat Analyzer
- 1
In WhatsApp choose Chat, then Export chat, then Without media.
- 2
Drop the exported text file onto the drop zone, or paste part of it into the box.
- 3
Check the detected date order and switch it manually if the dates look wrong.
- 4
Read the per-participant table, the hour and weekday histograms and the reply gaps.
About the WhatsApp Chat Analyzer
Export a chat from WhatsApp and this tool turns the resulting text file into statistics. It reports how many messages each participant sent and what share of the conversation that represents, along with word counts, average words per message, emoji use, media placeholders and deleted messages for each person.
Activity is broken down by hour of day and day of week as bar histograms, the busiest single day is identified, and the five longest waits between one person's message and the other's reply are listed with the names attached. Both the iOS bracketed export format and the Android dash format are handled, in 12- and 24-hour clocks.
Date order is detected automatically from any day above the twelfth, and can be overridden when the guess looks wrong. Your export is read 100% locally in your browser using the File API — it is never uploaded, and closing the tab discards it, which matters because a chat log is about as personal as a file gets.
Frequently asked questions
How do I export a WhatsApp chat?
Open the conversation, tap the chat name or the menu, choose Export chat and pick Without media. WhatsApp produces a plain-text file, usually named with a chat prefix and a .txt extension, and that is the file this tool reads.
Is my WhatsApp chat uploaded anywhere?
No. The file is read with your browser's File API and parsed on your own device, so its contents never travel over the network. Closing or reloading the tab discards everything that was loaded.
Why are some lines counted as system notices?
Lines without a name-and-colon prefix — group joins, encryption notices, security-code changes — are not messages from a participant, so they are counted separately and left out of the per-person statistics. The total is shown under the summary.
Why do the dates in my chat export look wrong?
Exports use the phone's local date order, so 01/08 can mean 1 August or 8 January. The tool guesses from any day above the twelfth, and on a short chat it may guess wrong — set the date order manually to correct it.
Why are my media or deleted message counts zero?
Those tallies rely on WhatsApp's English placeholder wording, such as the omitted-media and deleted-message notices. An export in another language uses different phrases, so the counts read low even though the messages themselves are still counted.
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