I am building a minimal behavior prediction engine called TiresiasIQ (v2) that:
- Logs user emotions/actions via a Streamlit GUI
- Extracts keywords, sentiment (TextBlob), and vector embeddings (spaCy)
- Combines them with normalized temporal cues (hour, weekday)
- Trains a simple 2-layer FFN using Keras on the sqlite3 db created
all through python
Then, given a natural query (e.g. “Will I cry at 11PM?”), it extracts the verb “cry,” embeds it, and predicts likelihood.
┌──────────────────────┐
│ User Log Input │ ◄───── "I feel empty after the breakup"
└─────────┬────────────┘
│
┌────────────▼────────────┐
│ NLP Feature Extractor │ (spaCy + TextBlob)
└────────────┬────────────┘
│
┌──────────────────────┼────────────────────────────┐
│ │ │
┌─────▼──────┐ ┌───────▼────────┐ ┌─────────▼────────┐
│ Keywords │ │ Sentiment │ │ Action Verb │
│ (Entities, │ │ (Polarity & │ │ Extraction via │
│ Lemmas) │ │ Subjectivity) │ │ VERB/NOUN Lemmas │
└─────┬──────┘ └───────┬────────┘ └─────────┬────────┘
│ │ │
└──────────────┬───────┴───────┬────────────────────┘
│ │
▼ ▼
┌───────────────────────────────┐
│ Temporal Features Extractor │ (Hour, Weekday)
└────────────────┬──────────────┘
│
▼
┌────────────────────────────┐
│ Feature Vector Builder │
│ [keywords + sentiment + │
│ action embedding + time] │
└────────────┬───────────────┘
▼
┌─────────────────────────────────┐
│ Feedforward Neural Network │
│ keras.Sequential([ │
│ Dense(32, relu) → Dense(1, σ) │
│ ]) │
└──────────────┬──────────────────┘
▼
┌────────────────────┐
│ Prediction Score % │ ◄──── "Cry: 78% at 11PM"
└────────────────────┘
(yeah mermaid didn't help)
It’s flat-featured for now (FFN only), but I’m running into some conceptual design questions:
How do you model semantic drift in action verbs over time of day?
E.g. “run” at 6AM = jog, “run” at 11PM = escape, “run” during a breakup = avoid
Given the evolving semantic drift of verbs over time and polysemy in user prompts, I want an idea for a good embedding strategy to capture temporal-semantic shifts in the meaning of actions, especially when grounded in subjective time-of-day behavior patterns like in the example. Any ideas would help but I'll pick up the pace after this year 🙏