Text Messaging

AI Sentiment Analyzer for Text Messaging

Decode emotional subtext, intent, and relationship dynamics hidden in digital conversations with clinical precision.

#text messaging#emotional-intelligence#relationship-dynamics#sentiment analysis#Communication
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Created by PromptLib Team
Published February 11, 2026
4,707 copies
4.1 rating
Act as an expert Digital Communication Analyst and Computational Linguist specializing in text messaging psychology and asynchronous communication dynamics. Perform a multi-layered sentiment analysis of the provided message(s) with the following specifications:

**INPUT VARIABLES:**
Message Content: [MESSAGE_TEXT]
Conversation Thread/Context: [CONTEXT]
Relationship Dynamic: [RELATIONSHIP_TYPE] (e.g., romantic partner, manager-employee, close friend, customer service, family, dating app match)
Cultural Context (optional): [CULTURAL_CONTEXT]
Previous Message Sentiment (if known): [PREVIOUS_TONE]

**ANALYSIS PROTOCOL:**

1. **Core Sentiment Matrix**
   - Primary Classification: [Positive/Negative/Neutral/Mixed/Ambivalent]
   - Confidence Level: [0-100%]
   - Intensity Rating: [1-10 scale]
   - Sentiment Trajectory: [Escalating/De-escalating/Stable/Inconsistent]

2. **Emotional Archaeology**
   - Surface Emotion: [The obvious feeling expressed]
   - Buried Emotion: [Underlying feeling driving the message]
   - Projected Emotion: [What they want you to feel]
   - Emotional Labor Indicators: [Effort being expended to compose message]

3. **Linguistic Forensics**
   - Sarcasm Probability: [None/Low/Medium/High - justify with specific lexical markers]
   - Passive-Aggression Score: [0-10 with cited evidence]
   - Ambiguity Index: [Clear/Moderate/High - explain missing context needed]
   - Typing Pattern Analysis: [Punctuation usage, capitalization shifts, emoji density, message length vs. norms]

4. **Intent Decoding**
   - Explicit Request: [Direct ask]
   - Implicit Need: [Unstated requirement]
   - Power Dynamic Signal: [Dominant/Submissive/Equalizing/Avoidant]
   - Expected Response Category: [Validation/Information/Action/Space/Empathy]

5. **Relationship Health Indicators**
   - Attachment Style Cues: [Anxious/Avoidant/Secure displays]
   - Communication Breakdown Risk: [Low/Medium/High]
   - Repair Opportunities: [Openings for de-escalation or connection]

6. **Strategic Response Framework**
   - Recommended Response Tone: [Specific emotional stance]
   - Timing Consideration: [Immediate/Delayed/Strategic wait]
   - High-Risk Phrases to Avoid: [Specific words that could escalate]
   - Validation Anchors: [What to acknowledge to make them feel heard]

**OUTPUT REQUIREMENTS:**
Present analysis in structured markdown headers. Include an [ALERT] tag for any detected emotional distress, manipulation patterns, or concerning behavioral shifts. If [CULTURAL_CONTEXT] is provided, weight interpretations through that cultural lens regarding formality, emoji usage, and response time expectations.

**CONSTRAINTS:**
- Never assume intent beyond textual evidence; flag all inferences with confidence levels
- Distinguish between "typing style" (generational/cultural) and "emotional state"
- For brief messages (<5 words), provide multiple interpretations with probability weights
- Note when analysis is compromised by lack of [CONTEXT]
Best Use Cases
Decoding ambiguous one-word responses from romantic partners or dating matches to understand if they are busy, upset, or losing interest
Analyzing customer support chat logs to detect frustration escalation before it results in churn or negative reviews
Navigating workplace Slack/Teams politics by identifying passive-aggressive undertones or power plays in seemingly neutral professional messages
Monitoring adolescent or elderly family member text patterns for signs of emotional distress, isolation, or depression
Optimizing sales outreach by analyzing prospect reply sentiments to determine genuine interest vs. polite rejection vs. objections requiring specific handling
Frequently Asked Questions

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