Semantic analysis is the critical task of the Natural Language Processing system to analyze the insightful information and grammatical formats of sentences from unstructured data. Some critical elements of semantic analysis in natural language processing are Hyponyms, Meronomy, Polysemy, and Antonyms. Synonyms and Homonyms.

Machine learning and deep learning techniques have been utilized for automated semantic analysis. Machine learning-based semantic analysis involves relationship extraction and word sense disambiguation. Semantic classification comprises topic classification, sentiment analysis, and intent classification. Semantic extraction involves keyword extraction and entity extraction. Some of the prominent applications of semantic analysis are:

•  Conversational chatbots: Chatbots are one of the semantic analyses empowered with keyword identification and conversational abilities. Conversational chatbots are furnished with emotional intelligence that detects the tone of the language and hidden emotions, enclosing sentimentally related relevant responses.

•  Automated ticketing support: Semantic analysis is an important component of automated ticketing support and helps in immediate action semantic analysis.

•  Sentiment analysis: Sentiment analysis understands sentiments and emotions from the text in the reviews and opinions on social media posts to systematically study the sentimental state of the user.

•  Search engine results: To impart accurate search results, search engines utilize semantic analysis to understand the user-s intention.

•  Language translation: Semantic analysis is effectively used in language translations with the advancement of natural language processing and deep learning. By analyzing the crucial parameters, such as the intent of the word or text, accurate language translation can be obtained.