Description
When it comes to extracting, analysing, and interpreting data across a wide range of fields, Natural Language Processing (NLP) has completely revolutionised the process. Through the process of allowing machines to comprehend, interpret, and synthesise human language, natural language processing (NLP) makes it possible to turn unstructured textual input into organised forms of information. A number of important natural language processing (NLP) techniques, including as tokenization, named entity recognition (NER), sentiment analysis, and text summarization, are discussed in this book, along with their applications in data-driven decision made. In addition, we highlight recent developments in deep learning models, such as transformer topologies (for example, BERT and GPT), which have increased natural language processing (NLP) performance dramatically. In addition, difficulties about the quality of the data, the presence of bias, and the effectiveness of the calculation are brought to light. In its conclusion, the article highlights the future potential of natural language processing (NLP) in domains such as healthcare, finance, and consumer analytics, all of which are areas in which datadriven insights play an important role
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