Documentation Index
Fetch the complete documentation index at: https://docs.threadlytics.io/llms.txt
Use this file to discover all available pages before exploring further.
Overview
Sentiment classifies each Reddit mention as Positive, Neutral, or Negative based on the language used in the post or comment. This gives you a signal for how Reddit users feel about a topic, not just how often it is being discussed.
Where It Appears
- Dashboard – Sentiment donut chart and Brand Sentiment summary card
- Sentiment Analysis page – full breakdown by keyword
- Conversations page – per-mention sentiment label and filter
- Keywords page – sentiment percentage column per keyword
- Top Sources page – average sentiment per user
How Is It Calculated?
Sentiment is determined by a custom keyword-based engine that runs automatically when a mention is saved.
Step 1: Normalize the Text
The mention’s title and body are combined and converted to lowercase so that “Amazing” and “amazing” are treated identically.
Step 2: Count Signal Words
The engine scans the normalized text and counts occurrences of words from three predefined signal lists:
| Signal Type | Example Words |
|---|
| Positive | amazing, awesome, excellent, fantastic, great, good, love, perfect, wonderful, effective, useful, helpful, easy |
| Negative | terrible, awful, horrible, bad, worst, hate, disgusting, disappointing, frustrated, useless, broken, bug, error, slow, poor |
| Question / Neutral | what, how, why, can, should, suggest, advice, opinion — plus every ? character in the text |
Step 3: Calculate Ratios
totalScore = positiveCount + negativeCount + questionCount
positiveRatio = positiveCount / totalScore
negativeRatio = negativeCount / totalScore
questionRatio = questionCount / totalScore
Step 4: Classify
The final classification is determined by comparing the ratios:
| Condition | Classification |
|---|
totalScore is 0 (no signal words found) | Neutral |
questionRatio > 0.4 and positiveRatio == negativeRatio | Neutral |
| Neither positive nor negative ratio exceeds 0.3, or they are too close | Neutral |
positiveRatio > negativeRatio AND positiveRatio > 0.3 | Positive |
negativeRatio > positiveRatio AND negativeRatio > 0.3 | Negative |
questionRatio > 0.4 and positiveRatio > negativeRatio | Positive (fixed score of 0.3) |
questionRatio > 0.4 and negativeRatio > positiveRatio | Negative (fixed score of -0.3) |
Sentiment Score
In addition to the label, a numeric score is stored ranging from -1.0 to +1.0:
- Positive score:
min(positiveRatio - negativeRatio, 1.0)
- Negative score:
max(-(negativeRatio - positiveRatio), -1.0)
- Question-dominant score: capped at ±0.3
This score is used elsewhere in the platform, including opportunity scoring.
How Sentiment Feeds Opportunity Score
Sentiment is one input into the Opportunity Score (0–100) that rates how worth engaging with a post is:
| Sentiment | Points Added to Opportunity Score |
|---|
| Positive | +10 points |
| Neutral | +5 points |
| Negative | 0 points |
Sentiment Aggregates
When sentiment is shown at the keyword or organization level, it is calculated as:
positiveMentions / totalMentions × 100 = positive sentiment %
Limitations
- The engine relies on a fixed vocabulary of signal words. Slang, sarcasm, and highly domain-specific language may be misclassified
- Very short posts with few words may default to Neutral due to a low
totalScore
- Sentiment is assigned at index time and is not automatically recalculated if the signal word lists are updated. A keyword reset and re-refresh is required to recompute sentiment for existing mentions.
