Longitudinal Sentiment Analysis of Personal Chat Logs

Monday, February 23rd 2026

Objective

Analyze ~36,000 personal WhatsApp messages to:

• Normalize and structure raw chat logs
• Store them in a queryable database
• Apply Spanish transformer-based sentiment analysis
• Aggregate results weekly
• Observe longitudinal emotional patterns

Parsing and Normalization

Input format

WhatsApp exports lines like:

23/07/2020, 11:59 - Facundo Ipharraguerre: Para gastar más

Parsing strategy

• Regex pattern:
  • Extract timestamp
  • Extract username
  • Extract message
• Handle multiline messages
• Filter unwanted content:
  • media placeholders
  • URLs
  • empty/symbol-only lines

Core regex:

r"^(\d{2}/\d{2}/\d{4}, \d{2}:\d{2}) - ([^:]+): (.+)"

Normalization decisions

• Preserve raw text
• Convert timestamps to ISO 8601
• Store clean text without system noise
• Do not lowercase globally (preserve signal for NLP)

Result: structured rows of:

(timestamp, participant, message)

SQLite Ingestion

Instead of per-user text files, use a relational model:

Tables

• conversations
• participants
• messages

Key design:

• Single messages table
• Foreign keys to participants and conversations
• Indexed by participant and timestamp

Benefits:

• Flexible aggregation
• Cross-user comparison
• Time-based queries
• Easy extension to MSN archives later

Temporal Behavioral Metrics

Before sentiment, basic structural analysis:

• Messages per participant
• Activity by quarter of day (00–06, 06–12, 12–18, 18–00)
• Monthly and weekly aggregation

This establishes behavioral baseline independent of emotional interpretation.

Transformer-Based Sentiment (Spanish)

Used pysentimiento, a Spanish NLP toolkit built on transformers.

For each message, the model outputs probabilities:

NEG;NEU; POS

Example:

"La comida está bien, pero el servicio es lento."→ NEG 0.72

Continuous Sentiment Index

Instead of categorical labels, derive a numeric signal:

sentiment\_index = POS - NEG

Properties:

• Range ≈ [-1, +1]
• Neutral messages ≈ 0
• Strong polarity weighted by model confidence

This avoids a crude +1 / -1 encoding.

Weekly Aggregation

Messages grouped by ISO week:

Computed per week:

• mean_sentiment
• std_sentiment (volatility proxy)
• mean_pos
• mean_neg
• message_count

Observed Patterns (in glorious Excel 2003)

From the output:

• Baseline slightly negative (~ -0.2)
• Consistent volatility (~0,067 stdev)
• Occasional strong positive spikes (on vacations)
• No extreme polarity drift...

Baseline Removal (“DC Coupling” Adjustment)

Initial weekly sentiment showed a persistent negative offset (≈ −0.2). This likely reflects:

• Conversational framing (complaints, problem-solving tone)
• Cultural linguistic patterns
• Model calibration bias
• Personal writing style

To focus on variation rather than absolute polarity, the weekly series was mean-centered:

sent_{normalized} = sentiment_{weekly} - \overline{sentiment}

This transformation:

• Preserves relative swings
• Removes stylistic bias
• Makes upward/downward deviations easier to see
• Allows comparison across different periods or platforms

Conceptually, this is equivalent to removing the DC component in signal processing.

After normalization:

• Zero represents your long-run expressive baseline
• Positive values represent relatively more positive weeks
• Negative values represent relatively more negative weeks

This makes the graph about dynamics, not personality labeling.

What This Project Demonstrates

• Personal archives can be structured into research-grade datasets
• SQLite is sufficient for longitudinal text analysis
• Transformer sentiment models can scale to tens of thousands of short texts
• Weekly aggregation provides stable signal
• Self-quantification via NLP is feasible without large infrastructure

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