Sentiment Analysis
Sentiment analysis is the use of NLP and often ML to detect the emotional tone or opinion in text (e.g., positive, negative, neutral). It is used in feedback, social listening, and customer insight.
In Simple Terms
Think of it as a mood ring for text: it reads the emotional tone of what people wrote.
Detailed Explanation
Models classify at the document, sentence, or aspect level (e.g., “the battery” in a review). Approaches range from lexicons and rules to fine-tuned or prompt-based LLMs. Output can be a label, a score, or a richer taxonomy (anger, joy, etc.). Sentiment analysis supports dashboards, alerts, and trend reports. Accuracy depends on domain, language, and nuance (sarcasm, mixed sentiment). It is a common first use case for text analytics in business.
Related Terms
RAG
Retrieval-Augmented Generation combines AI models with external knowledge retrieval for accurate responses.
Read moreDeep Learning
Deep learning is machine learning using neural networks with many layers. Depth allows models to learn hierarchical representations and has driven breakthroughs in vision, language, and other domains.
Read moreKnowledge Graph
A knowledge graph is a structured representation of entities (people, places, concepts) and their relationships, often stored as a graph database. AI can build, extend, or query knowledge graphs from text and other sources.
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