Semantic analysis involves several steps, including:
Text Preprocessing: This includes tokenization, stop-word removal, and stemming/lemmatization to prepare the text for analysis. Named Entity Recognition (NER): Identifying and classifying key entities such as materials, processes, and properties. Relation Extraction: Determining the relationships between different entities to understand how they interact. Topic Modeling: Identifying the main topics or themes within a large corpus of text. Sentiment Analysis: Understanding the sentiment or opinion expressed in the text, which can be useful for market analysis and trend prediction.
