Sentiment Analysis
An NLP technique that determines the emotional tone or opinion expressed in text, classifying it as positive, negative, or neutral, often with fine-grained intensity scores.
Sentiment analysis is one of the most widely deployed NLP applications in business. It processes customer reviews, support tickets, social media mentions, and survey responses to quantify how people feel about your product, brand, or specific features. Beyond simple positive/negative classification, modern sentiment analysis detects specific emotions (frustration, delight, confusion) and aspect-level sentiment (the UI is great but the pricing is frustrating).
LLMs have dramatically improved sentiment analysis accuracy, especially for nuanced cases involving sarcasm, mixed sentiment, and domain-specific language. A prompted LLM can match or exceed purpose-built sentiment models while also explaining its reasoning and handling custom sentiment categories without retraining.
Growth teams use sentiment analysis to monitor brand health across social channels in real time, prioritize support tickets by detecting frustration early, analyze product reviews to identify feature satisfaction and pain points, measure the emotional impact of marketing campaigns, and trigger automated responses when sentiment drops below thresholds. The key to production sentiment analysis is calibrating thresholds for your specific domain, since what counts as "negative" varies significantly across industries and contexts.
Related Terms
RAG (Retrieval-Augmented Generation)
A technique that grounds LLM responses in external data by retrieving relevant documents at query time and injecting them into the prompt context.
Embeddings
Dense vector representations of text, images, or other data that capture semantic meaning in a high-dimensional space, enabling similarity search and clustering.
Vector Database
A specialized database optimized for storing, indexing, and querying high-dimensional vector embeddings with sub-millisecond similarity search.
LLM (Large Language Model)
A neural network trained on massive text corpora that can generate, understand, and transform natural language for tasks like summarization, classification, and conversation.
Fine-Tuning
The process of further training a pre-trained LLM on a domain-specific dataset to specialize its behavior, style, or knowledge for a particular task.
Prompt Engineering
The practice of designing and iterating on LLM input instructions to reliably produce desired outputs for a specific task.