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{{see also|Machine learning terms}}
==Introduction==
In the field of machine learning, the '''bag of words''' (BoW) model is a common and simplified representation method used for natural language processing (NLP) and text classification tasks. The primary goal of the BoW model is to convert a collection of text documents into numerical feature vectors, which can be used as input for machine learning algorithms. 

==Methodology==
The bag of words model comprises two main components: vocabulary construction and text representation. 

===Vocabulary Construction===
The first step in creating a bag of words model is to construct a vocabulary, which consists of a set of unique words found in the given text corpus. This process generally involves the following steps:

1. Tokenization: Split the text into individual words, known as tokens.
2. Lowercasing: Convert all tokens to lowercase to ensure consistent representation.
3. Stopword removal: Remove common words, such as "a," "an," "the," and "is," which may not hold significant meaning in the context of the given problem.
4. Stemming or Lemmatization: Reduce words to their root form to minimize the number of unique words in the vocabulary while maintaining their meaning.

===Text Representation===
After constructing the vocabulary, the next step is to represent the text documents in the form of numerical feature vectors. Each document is represented by a vector, where each element corresponds to a word in the vocabulary. The value of the element can be the frequency, binary representation, or term frequency-inverse document frequency ([[TF-IDF]]) weight of the corresponding word in the document.

==Limitations==
While the bag of words model is simple and computationally efficient, it has certain limitations:

1. Order information: The bag of words model ignores the order of words in a document, which can lead to loss of contextual meaning.
2. Semantics: BoW does not take into account word meanings and semantic relationships between words.
3. Sparse representation: The feature vectors generated by the bag of words model are usually high-dimensional and sparse, which can lead to increased computational complexity and memory requirements.

==Applications==
Despite its limitations, the bag of words model is widely used in several NLP and text classification tasks, such as:

1. Sentiment analysis: Determining the sentiment (e.g., positive, negative, or neutral) of a given text.
2. Topic modeling: Identifying the underlying topics in a collection of documents.
3. Document classification: Assigning predefined categories to documents based on their content.

==Explain Like I'm 5 (ELI5)==
Imagine you have a bag filled with different types of toy blocks, each representing a unique word. Now, let's say you have a storybook, and you want to represent the story using the blocks from the bag. In the bag of words model, you would take out the blocks (words) from the bag and count how many times each block (word) appears in the story. Then, you would create a list with these counts, and that list would represent the story. This method helps computers understand and process text, but it doesn't care about the order of the words or their meanings.

[[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]