Semantic Search

What do you mean by Semantic Search?

Semantic search is a search technique powered by artificial intelligence (AI) that focuses on understanding the context and intent of a query rather than relying solely on keywords to provide a response.

Semantic search algorithms leverage various AI components and techniques such as natural language processing (NLP), natural language understanding (NLU), named entity recognition (NER), knowledge graphs, and semantic clustering to execute search tasks. NLP and machine learning (ML) aid in extracting keywords and organizing them into semantic clusters. This semantic categorization enables semantic search algorithms to comprehend the intent behind a search and surpass exact lexical matches.

Unlike traditional searches that heavily rely on string fields or keyword matches, semantic search incorporates multiple tasks like part-of-speech (POS) tagging, error correction, synonym identification, topic and aspect mapping, and others to analyze text. This enables semantic search to deliver highly precise results by extracting the most relevant information from multiple sources.

When applied to sentiment analysis, semantic search disregards irrelevant data and focuses on identifying and collecting data points that may not be an exact lexical match but align with the intended sentiment.

This aspect is particularly crucial in sentiment analysis when analyzing unstructured content such as social media comments, posts, reviews, and open-ended survey responses. The effectiveness of semantic clustering directly impacts the accuracy of sentiment analysis results for such data.