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A bag-of-words model is a common way to represent text data in natural language processing (NLP). In this model, a document is represented as an unordered set or "bag" of its words, disregarding grammar and word order but keeping track of the frequency of each word. This representation is used for various NLP tasks like text classification, sentiment analysis, and information retrieval. Bag-of-words model converts the phrases or sentences and counts the number of times a similar word appears. The bag of words technique is actually called CountVectorizer, which means counting how many times each word appears and puts them into a vector.

To build a bag-of-words model, a few things which need to be taken care of are:
         i) Lowercase every word
         ii) Drop punctuation
         iii) Drop very common words (stop words)
         iv) Remove plurals, e.g. students => student
         v) Perform lemmatization, e.g. reader => read, reading = read
         vi) Use n-grams, e.g. bigrams (two-word pairs) or trigrams
         vii) Keep only frequent words, e.g. must appear in >10 examples
         viii) Keep only the most frequent M words, e.g. keep only 1,000
         ix) Record binary counts (1 = present, 0 = absent) rather than true counts
         x) Find other better combinations for best practice.

Word2Vec models usually performs better than simple bag of words models. A bag of words model only counts how many times each word appears in each document. The bag of words models have no information about how similar the words are. Word2Vec can figure out that some words are similar to each other and then it performs better when doing machine learning with text.

Multinomial Event Model is a statistical model used in various fields, including natural language processing and information retrieval. It is primarily employed for modeling and analyzing text data, making it particularly relevant in text classification, document retrieval, and related tasks. This model is a simplified version of the more general and widely used Bag of Words (BoW) model.

The bag-of-words model is simple and computationally efficient, but it lacks the sequential and structural information present in the original text. Despite its limitations, it serves as a foundation for more advanced text processing techniques. 

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Text classification/sort/prediction, train/test e.g. Youtube spam: (code)

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