Unveiling Text Representation with 'Bag of Words'

Abstract

Techniques for natural language processing (NLP) have grown to be essential tools for deciphering and drawing insightful conclusions from massive volumes of text data. A thorough review of numerous natural language processing (NLP) techniques, including as tokenization, stemming, lemmatization, named entity recognition, sentiment analysis, and topic modelling, is provided in this abstract. These methods are essential for applications like sentiment analysis, machine translation, text assistant categorization, and information retrieval. Furthermore, the capabilities of NLP systems have been greatly improved by recent developments in deep learning, especially with models like BERT and GPT. This has allowed them to reach state-of-the-art performance in a variety of language understanding tasks. The difficulties and potential paths for future study in NLP, including managing ambiguity, comprehending context, and enhancing multilingual assistance, are also highlighted in this abstract. Using NLP tools to their full potential, researchers.