Intro

Day 9. Today is less about model internals and more of a personal experiment: have a local AI analyze the entire chat history with one LINE friend. (LINE is the dominant messaging app in Japan.)

When I exported it, 8 years were sitting there — from the very first message to today. It started, we talked a lot, it went quiet for a while, then picked up again. That whole arc is in there.

Because the content is what it is, nothing left my machine: everything ran locally on my DGX Spark.

What I used: my home AI box (DGX Spark) + a Japanese sentiment model (for tone) + a bigger local model (to guess events from numbers).

Today's setup

What I wanted to do

Re-reading 8 years of messages one by one isn't realistic. So instead of reading the content, I looked only at the "shape" of the conversation — when, how much, and in what tone we talked.

Concretely:

• monthly message volume


• the trend of tone (positive / negative)


• then asking an AI to find "when something big happened"

Heads-up (the result)

From message counts and tone alone, the 8-year arc came out clearly on a chart. Started, went quiet, came back — the flow was visible without me re-reading a thing.

🔧 Pipeline

LINE chat export (text)

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 1. Parse: split each message into {datetime, who, type, text}

        │   (from here on, message text never leaves the machine)

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 2. Aggregate: monthly counts, time-of-day, reply gaps

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        ▼

 3. Tone scoring: classify each of 66k messages pos/neu/neg

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 4. Turning-point detection: from sudden changes in the numbers

        │   + also show ONLY the numbers to a bigger AI and ask it to guess

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 5. Answer check: compare against the real timeline

You can export a LINE chat as text from the chat screen ("send chat history").

Data size:

15,605 stickers… that's a lot.

The two AIs

Both run locally on my own machine.

📊 Results

The 8-year arc of volume and tone

This chart is the highlight. Top: monthly message count. Bottom: tone (up = positive, down = negative). The x-axis is months since the conversation started. (Axis labels are in Japanese.)

A clear concentration at 7–9 a.m.!

Could the AI guess the turning points?

First, the simple method: mechanically pick the points where message volume jumped or dropped, then check against the real timeline.

Sharp volume changes were nailed. But "a big life milestone" got missed. So I showed the same numbers to the bigger local model and asked "what happened?" — and got back:

• "around when it started" → roughly matches


• "a stretch of going silent" → matches the quiet period


• "a major life change" → almost exactly before the real milestone

Rather than hunting for a single spike, it reads the whole sequence of numbers as a "flow," so it could pick up even an event that barely moves the counts.

💡 Takeaways

1. Volume + tone alone reveal the arc

Counts and tone were enough to see the 8-year shape. Silence marks the quiet stretch; a surge marks the resumption — straight off the chart.

2. A local model reads a story out of numbers

Given only monthly numbers, the model inferred even a barely-visible event ("something big around here"), and it lined up with reality. It connects scattered points into one flow.

3. A "negative" tone doesn't mean a bad relationship

The slight negative lean in later years isn't about getting along badly. Logistics messages ("what time are you home?") just don't score high. Low score ≠ trouble. It isn't that sentiment analysis is poor — the scores need to be read together with context.

🛠️ Technical details

Parsing & aggregation

• LINE export format is a date header plus time<TAB>name<TAB>text. Multi-line messages (4,987 of them) are merged back into the previous message.


• Speakers normalized to "A / B" by message count (no real names in anything public). Temporary group members and system lines excluded.


• Messages tagged by type (text / sticker / photo / call / unsent…). Tone uses text only; volume counts use all types.


• Aggregation and plotting in Python (pandas / matplotlib).

Tone (sentiment)

• 
  koheiduck/bert-japanese-finetuned-sentiment, a 3-class (pos / neu / neg) Japanese model.


• 66,329 texts scored on GPU in batches; per message I take P(pos) − P(neg) in [−1, +1], then average per month.

Turning-point detection

• Rule-based: long near-zero stretches (silence), large month-over-month surges, and tone peaks — all from numbers only.


• Plus: the per-month table of counts + tone scores fed to a bigger local model (Qwen2.5-72B via ollama) to guess events. No message text was given.


• Real event dates were kept in a local note only, used for annotation and the answer check.

Privacy

• Every file containing message text (raw export, parsed data, scores) stays in a non-public folder.


• Only aggregate numbers and charts are published. The chart x-axis is relativized to "months since the conversation started," hiding actual dates.


• Apart from a few short, name- and place-free one-liners shown as scoring examples, no conversation content, real names, specific dates, or long text appears in the article or charts.

Tomorrow: Day 10

Weather forecasts say one temperature, but everyone feels it differently. Same degrees, different "do I need a coat?" So next I'm building my own personal "weather officer" AI: from past weather data, it'll tell me each morning something like "coat + beanie today." Over the next 100 days I'll teach it my own sense of cold — the start of a longer project.

100ExperimentsWithDGX #LocalLLM