Most sentiment analysis tools apply the same generic positive, negative, neutral model to every kind of text. That works reasonably well for product reviews. It works poorly for claims calls, churn-risk customer interactions, or patient-reported adverse events, because the signal that actually matters in each of those contexts is domain-specific, not generic.
A customer who says "fine, I guess" in a churn-risk context is a much stronger signal than the words alone suggest. A claims caller who hesitates before answering a specific question is a different signal than a frustrated tone. A patient describing a symptom in particular language may be describing an adverse drug reaction before any clinical code captures it.
Generic sentiment models miss all of this, because they were never built for the specific use case. That is the gap we built Pulse to close: sentiment analysis tuned to the actual problem, churn detection, claims fraud indicators, adverse event language, organizational morale, or real-time customer experience, not a one-size-fits-all positive or negative score.
Samir Abu Ghosh