Semantic search is an AI-enabled search technique that uses context and intent to understand a query rather than relying on keywords to provide an answer.

Semantic search algorithms are used by other AI branches and techniques like natural language processing (NLP), natural language understanding (NLU), named entity recognition (NER), knowledge graphs and semantic clustering to perform search tasks. NLP and machine learning (ML) help in keyword extraction and categorize them into semantic clusters. This semantic classification facilitates semantic search algorithms to understand search intent and go beyond exact lexical matches.

Unlike traditional searches that depend on string fields or keyword matches, semantic search employs several tasks such as part-of-speech (POS) tagging, error correction, synonyms, topic and aspect-mapping and others to analyze text. This allows it to present highly precise results based on the most relevant details from multiple sources.

When applied in sentiment analysis, it excludes irrelevant data while identifying and gathering datapoints that are not an exact lexical match but match intent.

This is a key requirement in sentiment analysis to analyze free-form, unstructured content such as social media comments, posts, reviews and open-ended answers in surveys. The more robust semantic clustering is, the more accurate the results are for data sentiment.