The intent of the BoW approach is to convert the review text provided into a matrix form. It represents documents as a set of distinct words by ignoring the order and meaning of the words. Each row of the matrix represents each review (otherwise called a document in NLP), and the columns represent the universal set of words present in all the reviews. For each document, and across each word, the existence of the word, or the frequency of the word occurrence, in that specific document is recorded. Finally, the matrix created from word frequency vectors represents the documents set. This methodology is used to create input datasets that are required to train the models, and also to prepare the test dataset that need to be used by the trained models to perform text classification. Now that we understand the BoW motivation...
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Dr. Sunil Kumar Chinnamgari has a Ph.D. in computer science and specializes in machine learning and natural language processing. He is an AI researcher with more than 14 years of industry experience. Currently, he works in the capacity of lead data scientist with a US financial giant. He has published several research papers in Scopus and IEEE journals and is a frequent speaker at various meetups. He is an avid coder and has won multiple hackathons. In his spare time, Sunil likes to teach, travel, and spend time with family.
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Dr. Sunil Kumar Chinnamgari has a Ph.D. in computer science and specializes in machine learning and natural language processing. He is an AI researcher with more than 14 years of industry experience. Currently, he works in the capacity of lead data scientist with a US financial giant. He has published several research papers in Scopus and IEEE journals and is a frequent speaker at various meetups. He is an avid coder and has won multiple hackathons. In his spare time, Sunil likes to teach, travel, and spend time with family.
Read more about Dr. Sunil Kumar Chinnamgari