A sentiment analysis model is deployed in 2023. It performs well on validation data. 88% accuracy. The organization is confident.
By 2025, the model is still deployed. Nobody has retrained it. The world has changed. Language has evolved. The data distribution has shifted. But the model makes predictions the same way it always did.
The accuracy has probably declined to 75%. Maybe lower. But nobody knows because nobody measures it on fresh data. The model still outputs confidence scores. The scores still look normal. The organization assumes the model is still working.
Then something breaks. The organization discovers that the model has been systematically wrong for months. But the discovery is accidental. They were not measuring.
Sentiment model decay is invisible. It happens gradually. The model stops being trained. The world changes. The gap grows. The organization does not notice until the consequences are unavoidable.
Concept Drift
Concept drift is when the underlying data distribution changes over time.
A sentiment model is trained on product reviews from 2023. It learns to associate certain words and patterns with positive and negative reviews.
By 2025, the world has changed:
- New products exist (the model does not know how to categorize them)
- Customer expectations have changed (what counted as good service in 2023 is different now)
- Competitor offerings have changed (product comparisons are different)
- Customer demographics have shifted (new customer segments with different language patterns)
The model was optimized for the 2023 distribution. When you apply it to 2025 data, it is out of distribution. Accuracy declines.
But the model does not know this. It still outputs confidence scores. The scores look normal. The organization assumes accuracy is maintained.
Concept drift happens continuously. It is not a one-time shift. The distribution is always changing slightly. Over months and years, these small changes accumulate. The model gradually becomes less accurate.
Specific Types of Drift
Temporal drift. Language changes over time. Words shift meaning. New slang emerges. Old slang disappears.
In 2020, “sick” meant bad. In 2024, “sick” often means good. A model trained on 2020 data sees “sick” and predicts negative. On 2024 data, it is backwards.
Similar shift for “woke” (original meaning about social awareness, now often used pejoratively), “lit” (slang for exciting), “cap” (slang for lie). These shift meanings or emerge as new slang.
A model trained on 2020 language does not have embeddings for new slang. It encounters “no cap” (meaning “no lie” or “for real”) and does not know how to classify it. It makes a guess based on partial patterns. The guess is often wrong.
Domain drift. The domain changes. A sentiment model trained on luxury product reviews does not generalize to budget product reviews.
A sentiment model trained on cloud infrastructure reviews (where uptime and performance are paramount) does not work well on SaaS application reviews (where usability matters more).
If a model is deployed across multiple domains without retraining for domain-specific patterns, accuracy declines as the domain changes.
Population drift. The population generating the text changes.
A sentiment model trained on reviews from early adopters does not work well on reviews from mainstream users. Early adopters are enthusiastic and forgiving. Mainstream users are pragmatic and critical.
A company launches in a new geographic market. Customers write reviews in different language patterns. Different cultural norms about expressing satisfaction. The model trained on the original market does not work well on the new market.
If the model is not retrained on the new population, accuracy declines.
Annotation drift. The way sentiment is labeled changes.
This is subtle. A sentiment model is retrained on new data. But if the new data is labeled differently (different annotators, different criteria), the model learns new associations.
A company’s support team changes how they label escalations. Previously, they labeled complaints as negative. Now they label them as urgent-but-constructive. The same underlying sentiment is labeled differently.
If a sentiment model is trained on the old labels, it does not work well on newly labeled data.
Why Drift Is Invisible
Drift is invisible because organizations do not measure it.
No validation on fresh data. Organizations validate a model on test data when it is deployed. Then they stop validating. They do not measure accuracy on new data regularly.
If they measured accuracy on fresh data every month, they would see it declining. But most organizations do not do this.
Metric stability masks decline. A sentiment model outputs scores. The scores look normal. The distribution of scores looks similar to before. The organization assumes the model is working.
But the scores are wrong more often. The distribution of scores can stay the same while accuracy declines.
Imagine a model that always outputs 0.6 (middle of the range). The distribution of outputs is stable. Accuracy could be 50%. The organization sees stable metrics and assumes stable accuracy.
No measurement against outcomes. Organizations do not measure whether sentiment scores predict actual behavior.
If sentiment is high, do people actually stay? If sentiment is low, do people actually leave? Most organizations do not measure this.
If they did, they would discover that the correlation has weakened. The model’s predictions are no longer predictive.
Confidence remains high. The model still outputs confidence scores. A confidence score of 0.85 looks reliable. The organization trusts it.
But the confidence is calibrated to the training data, not to current data. As data distribution shifts, confidence becomes miscalibrated. The model is confident and wrong.
No feedback loop. Unlike some systems, sentiment analysis does not have an obvious failure signal.
A recommendation system has a feedback signal: people either follow the recommendation or they do not. A fraud detection system has a signal: the flagged transaction either turns out to be fraud or it does not.
Sentiment analysis has no obvious feedback. If the model says sentiment is positive and the person leaves, nobody necessarily connects these. The person might have left for unrelated reasons.
Organizations have no feedback signal that the model is wrong.
The Compounding Problem
Drift compounds over time. The longer a model is deployed without retraining, the worse it gets.
A model trained in 2023 works reasonably well in 2023. By mid-2024, it is slightly out of distribution. By end-2024, it is meaningfully out of distribution. By 2025, it might be substantially wrong.
But the organization keeps using it. They have invested in deploying it. Retraining requires effort. So they keep using the aging model.
As the model decays, its predictions become less useful. But the organization does not notice because they are not measuring.
Over time, the gap between what the model predicts and actual reality grows. The organization is making decisions based on increasingly inaccurate signals.
Then something breaks. A crisis. An exodus. A major miscalculation. Only then does the organization discover the model has been wrong for months.
Why Organizations Do Not Retrain
Retraining a sentiment model requires:
New labeled data. You need recent text labeled with sentiment. This requires collecting feedback and paying annotators to label it.
Most organizations do not want to spend this. It is an ongoing cost. They already invested in the initial model. Retraining feels like throwing money away.
Model evaluation. You need to evaluate the new model. Compare it to the old one. Measure performance on fresh data.
This requires data science effort. Organizations often do not have dedicated people for this. The person who built the model has moved to another project.
Redeployment. You need to deploy the new model. Update pipelines. Test the new model in production.
This adds operational overhead.
Maintenance commitment. Retraining implies a commitment to maintain the model. Retrain every quarter. Every month. Evaluate regularly. Keep the model fresh.
Most organizations do not want to commit to this. They want a set-it-and-forget-it solution.
The Cost Justification Problem
The case for retraining is hard to make.
The benefit of retraining is “the model will be more accurate.” But if the organization is not measuring accuracy, they do not know accuracy is declining. From their perspective, the model is working fine.
The cost of retraining is clear: people time, annotation cost, deployment effort.
From a business perspective, “spend money to make a model better that you think is already fine” is hard to justify.
So organizations do not retrain. They keep using the aging model. The model decays. Eventually it becomes obviously wrong. Then there is a crisis.
After the crisis, there is a push to fix it. Retraining happens. The model improves. Confidence is restored.
But the organization usually does not commit to ongoing maintenance. They do not set up regular retraining. The cycle repeats.
The False Signal Problem
As a model drifts, it often produces false signals that the organization acts on.
A sentiment model is trained on 2023 data. By 2025, it is systematically biased in a particular direction.
Maybe it systematically over-estimates positive sentiment (because new slang that it interprets as positive is actually neutral). The organization measures rising sentiment and thinks things are improving. They are not. The model is misclassifying.
Or maybe it systematically under-estimates positive sentiment. The organization measures declining sentiment and thinks things are worsening. They are not. The model is misclassifying.
Based on false signals, the organization makes decisions:
- Adjusting strategy based on false sentiment trends
- Changing hiring or retention practices based on false employee sentiment
- Adjusting marketing based on false customer sentiment
The decisions are based on a drifted model that the organization does not know is drifted.
The Catastrophic Failure
Drift eventually becomes obvious.
An employee is measured as highly satisfied by the sentiment model. But they are actively job searching. They leave. The organization is shocked. “Sentiment was high.”
A customer segment is measured as positive by the model. But they are churning at high rates. The organization loses them. “Sentiment was positive.”
A team is measured as highly engaged by the model. But they are burned out. They all quit. “Engagement was high.”
In each case, the sentiment model was drifted. It was systematically wrong. But the organization did not know until the outcome failure forced them to look closer.
When Drift Becomes Obvious
Drift is usually discovered through outcome failure, not through measurement.
Someone notices that sentiment prediction does not match behavior:
- “Sentiment is high but people are leaving”
- “Sentiment was positive but the customer churned”
- “Engagement scores are good but the team is underperforming”
This mismatch prompts an investigation. The investigation discovers the model is out of date.
Most organizations do not measure this proactively. They discover it reactively, after damage is done.
How To Detect Drift
If you want to catch drift before it becomes catastrophic:
Measure accuracy regularly. On a monthly basis (at minimum quarterly), measure model accuracy on fresh data.
Keep a holdout test set of recent data that the model was not trained on. Measure accuracy on it regularly. Track whether accuracy is declining.
Measure against outcomes. For each prediction, measure whether it correlates with actual behavior.
If the model predicts high sentiment, do those people actually stay? Are they actually engaged? Measure the correlation.
Track prediction distribution. Monitor the distribution of predictions over time. Is the model outputting the same range of scores? Or is it drifting toward always-positive or always-negative?
If the distribution is drifting, the model is probably drifting.
Set up automated retraining. Establish a schedule for retraining. Every quarter, collect new labeled data and retrain the model.
Commit to maintenance. Treat it as an ongoing cost.
Validate new data. When data distribution has obviously changed (new market, new product category, new platform), validate the model on that new data before deploying it.
Document assumptions. The model was trained on specific data (time period, domain, population, labeling scheme). Document these assumptions. When any of these change significantly, the model becomes at-risk for drift.
Why This Rarely Happens
Most organizations do not do these things because:
Effort. It requires people to maintain the model. Most organizations do not want to commit ongoing resources.
Cost. Collecting new labeled data costs money. Retraining costs compute. Ongoing monitoring costs time. Organizations want a one-time cost, not an ongoing one.
Invisibility. Drift is invisible if you are not looking for it. The metric looks fine. The model looks fine. Nobody feels urgency to maintain it.
Competing priorities. The person who built the model has moved on. The model is “working” (in the sense that it is running and producing output). Maintenance feels like a low priority compared to new features or new projects.
The Deeper Issue
Sentiment analysis is attractive partly because it promises to replace human judgment with an automated system. Once built, it should work forever.
But models do not work forever. They decay. They require maintenance. They require retraining. They require ongoing evaluation.
The organizations that succeed with sentiment analysis are the ones that treat it as a system to maintain, not a one-time tool to deploy.
But most organizations treat it as a tool. They build it. They deploy it. They expect it to work. When it decays, they are surprised.
The solution is not to build a better model. The solution is to commit to maintaining it.
Most organizations are not willing to make this commitment. So they deploy models that gradually decay. They do not notice until outcomes fail.
The Alternative
If you are not willing to maintain a model, do not deploy it.
Either commit to:
- Quarterly retraining
- Monthly accuracy measurement
- Regular validation on new data
- Ongoing monitoring
Or do not use automated sentiment analysis.
Use manual processes instead. Have people read feedback. Have people understand sentiment. Have people adapt as language and context change.
This is not scalable. But it does not decay. It adapts naturally as the world changes.
The organizations that have robust understanding of customer or employee sentiment are usually the ones that do this manually, not the ones that rely on aging models.
The organizations that rely on models and do not maintain them are the ones that are systematically wrong without knowing it.