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Literature Survey in Semantic Analysis

Literature Survey in Semantic Analysis

Great Literature Survey in Semantic Analysis

Semantic analysis is a significant feature of natural language processing that assess and represents human language, beneficial to analyze texts in natural languages for the understanding of human being. Some popular semantic analysis approaches are Latent Semantic Analysis, Explicit Semantic Analysis, and Sentiment Analysis (SA).

Semantic Analysis assists machines in analyzing texts meaning and extracting beneficial information to recognize the relationship between the words in a specific context. Applicative areas of semantic analysis are social networks, healthcare, agriculture, the Internet of Things, and computer vision. Hyponyms, Meronomy, Polysemy, Antonyms. Synonyms and Homonyms are elements of semantic analysis.

Conversational chatbots, automated ticketing support, Sentiment analysis, Search engine results, text analytics, and Language translation are some of the applications in semantic analysis. Recently, machine learning and deep learning algorithms have been applied for semantic analysis in various application scenarios.

Several surveys on semantic analysis have been published that describe semantic analysis approaches, application fields, future scopes, limitations, and challenges.