Semantic search is an AI-enabled search method that utilizes context and intent to further understand a question rather than relying on its keywords to provide an response.
Semantic search algorithms are utilized by other AI branches and techniques like natural language understanding (NLU), knowledge graphs, named entity recognition (NER), natural language processing (NLP), and semantic clustering to perform search tasks. NLP and machine learning (ML) help in keyword extraction and categorize them into semantic groups. This semantic classification enables semantic search algorithms to further understand search intentions and go beyond exact word choice matches.
Unlike traditional searches that rely on string fields or keyword matches, semantic search utilizes several methods like part-of-speech (POS) tagging, error correction, synonyms, subject and aspect-mapping and others to understand what is written. This allows it to present high precision results founded on the most important details from multiple pathways.
When applied in sentiment analysis, it does not consider irrelevant data while identifying and gathering datapoints that are not an exact word choice match but match search intention.
This is a crucial requisite in sentiment analysis, to analyze free-form, loosely structured content such as open ended content in social media comments or in surveys. The more ironclad and thorough semantic clustering is, the results are for data sentiment are sure to become more accurate.