Sentiment Analysis
Definition
Sentiment analysis is the natural-language processing technique used to classify a customer review as positive, negative, neutral, or mixed, and to identify specific emotional or topical signals within the text — used by review platforms, reputation management tools, and AI-powered review responders.
Modern sentiment analysis uses transformer-based language models (BERT, RoBERTa, or domain-specific finetunes of foundation models like Gemini or GPT-5) to score review text on multiple dimensions simultaneously. The model produces:
- Polarity — positive, negative, neutral, or mixed. - Intensity — how strongly the sentiment is expressed. - Aspect signals — what specifically the customer praised or complained about (food, service, price, ambience, etc.). - Emotion classification — frustration, disappointment, delight, surprise, gratitude.
For review response tools, sentiment analysis enables automatic mode switching. ReplyWithCare's generator detects negative sentiment in the pasted review and activates the Negative Review Handler with the HEARD framework. It detects multi-aspect complaints and ensures the reply addresses each aspect, not just the dominant emotion.
Sentiment analysis has known weaknesses: sarcasm, mixed-tone reviews, and culture-specific expressions (Hinglish reviews with mixed Hindi-English emotional words) can confuse general-purpose models. Production systems either fine-tune the model on domain-specific reviews or use multiple model outputs and combine them for higher confidence.
Related terms
HEARD Framework
The HEARD framework is a five-step approach to responding to negative customer feedback — Hear, Empathize, Apologize, Resolve, Diagnose — adapted from luxury hospitality (notably the Ritz-Carlton) and applied to written review responses.
Negative Review
A negative review is any customer review rated below the platform's neutral midpoint — typically 1-2 stars on Google, Yelp, or Facebook — that requires structured response handling to avoid further damage to the business's rating, ranking, and conversion rate.