Ranked topic counts
Sort matched topics by the number of attributable text messages containing at least one configured keyword.
Private keyword-based WhatsApp topic analyzer
Find recurring topics in a WhatsApp chat
See which tracked subjects appear most often in an exported conversation, who mentions them, when they first appeared and how their monthly activity changes. MyChatWrap uses a transparent English keyword dictionary—not semantic AI classification.
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How does the WhatsApp Topic Analyzer work?
MyChatWrap checks attributable text messages against fixed, case-insensitive whole-word keyword lists for 18 topics. One message may match several topics, while paraphrases and ambiguous meanings may be missed or misclassified. The result is a deterministic keyword signal, not an understanding of what the conversation means.
The real Dashboard › Overview Topic Intelligence panel
The preview renders the production TopicIntelligenceSection with fictional topic counts, participant attribution, first mentions, monthly sparklines, shifts and reply-time context.
Product preview
Fictional exampleTracked topics found
0
Top matched topic
Food · 184
Fastest topic reply median
Food · 1m 12s
Topic intelligence
What this chat actually talks about, and how that's shifted over time
Jun 2025's obsession
Food — 120 mentions (+500% vs. May 2025)
Food
184First came up Jan 8, 2024, brought up by Person A
Talked about most by Person A
Median reply time here: 1m 12s
Work
143First came up Jan 12, 2024, brought up by Person B
Talked about most by Person B
Median reply time here: 7m 0s
Travel
112First came up Feb 3, 2024, brought up by Person A
Talked about most by Person A
Median reply time here: 2m 30s
Music
88First came up Mar 11, 2024, brought up by Person B
Talked about most by Person B
Median reply time here: 3m 30s
Movies & TV
73First came up Apr 5, 2024, brought up by Person A
Talked about most by Person A
Median reply time here: 3m 0s
Family
61First came up Feb 18, 2024, brought up by Person B
Talked about most by Person B
Median reply time here: 5m 30s
Topics by year
The top topics discussed each year
2024
- Work80
- Travel49
- Food44
2025
- Food140
- Work88
- Travel63
Topic shifts
Topics that suddenly spiked or dropped off, month over month
Food picked up in Jun 2025 — 20 mentions in May 2025 → 120 in Jun 2025 (+500%)
Travel picked up in Jul 2024 — 14 mentions in Feb 2024 → 35 in Jul 2024 (+150%)
Travel picked up in Jun 2025 — 18 mentions in May 2025 → 45 in Jun 2025 (+150%)
Work dropped off in Jun 2025 — 64 mentions in May 2025 → 24 in Jun 2025 (-62%)
Fastest & slowest topics to reply about
Replies about 🍔 Food land 83% faster than replies about 💼 Work — 1m 12s vs. 7m 0s median.
Starting conversations vs. introducing topics
Kicking off a conversation isn't the same as bringing up a new subject — here's how each person's share compares
Person A starts 68% of conversations, introduces 50% of topics
Person B starts 32% of conversations, introduces 50% of topics
Participant attribution
Show who contributed the most matched messages for each topic and who introduced it first in the export.
Monthly topic trends
Plot mention counts by month, compare leading topics by year and surface qualifying spikes or drops.
Reply-time context
Calculate the median eligible response time for replying messages that themselves match each topic.
Starting versus introducing
Contrast each participant's share of conversation starts with their share of first topic mentions.
Evidence links
Jump to the first matching message when its private dashboard message identifier is available.
Analyze WhatsApp topics in three steps
1. Export the conversation
Choose Export chat in WhatsApp and save the .txt or .zip containing the period you want to study.
2. Add it privately
Drop the export above. The browser matches eligible English text against the published topic dictionary.
3. Review patterns and evidence
Compare counts, participants, months and reply context, then inspect first-mention evidence before interpreting a pattern.
How to export a chat from WhatsApp
Two minutes on your phone. Choose your device, follow the taps, then come back and add the file above.
Watch the whole flow — export from WhatsApp, then drop the file into this app.
What topics and keywords are tracked?
The current dictionary covers Travel, Work, Family, Money, Food, Health, Relationship, Movies & TV, Sports, Gaming, Music, School, Technology, Weather, Shopping, Home, Pets and Celebrations. Each category has a fixed English keyword list in the analytics code.
Matching is case-insensitive and uses word boundaries. The analyzer intentionally avoids broad gaming terms such as game and play because they create many false positives in sports and everyday speech.
How messages become topic counts
Only attributable entries parsed as text are eligible. A message adds at most one count to each topic whose regular expression matches, but the same message can count toward multiple topics—for example, work trip can match both Work and Travel.
A count represents matching messages, not keyword occurrences. Repeating coffee three times in one message still adds one Food message, while a system or media entry adds none.
How reply and trend context is calculated
A topic's median reply time uses only matched messages that the main reply engine already identified as replies. It means how fast replies that were themselves about the topic arrived—not how fast someone answered a question about that topic. The fastest-versus-slowest comparison requires at least two topics, at least three matched messages in each displayed topic and a real median difference.
Topic shifts compare adjacent populated monthly points. A shift needs meaningful volume: at least one side has three mentions, spikes require at least +150% and drops require -60% or lower. The latest-month obsession uses the latest month in the export, not today's calendar month.
Why keyword matching is not semantic understanding
The analyzer can miss paraphrases that contain no tracked word, and a matched word can have another meaning in context. It does not use embeddings, a language model, sentiment inference or server-side content analysis.
Treat every topic as a discoverable keyword pattern. Use the first-mention link and surrounding messages to verify examples before drawing a conclusion.
What the topic analyzer shows
- Matched-message counts and participant attribution for tracked English topics.
- First mentions, monthly counts, annual leaders and qualifying shifts.
- Median reply context and conversation-start versus topic-introduction shares where data qualifies.
What it cannot show
- Every subject discussed through paraphrases, slang or unsupported languages.
- The intended meaning of an ambiguous keyword or the sentiment behind it.
- A semantic summary, psychological profile or authoritative interpretation of the conversation.
Analyze topics without uploading the conversation
MyChatWrap performs topic matching and aggregation in your browser. Message text, participant names and the filename are not sent to or stored on the MyChatWrap server, and no account is required.
WhatsApp topic analyzer questions
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See what keeps coming up in your chat
Add an exported WhatsApp conversation to explore tracked topics, participants, monthly patterns and reply context.
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