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What are some use cases of Bag of Words model?

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Some of the popular use cases of the bag of words model is listed below:

  • Document Similarity
    It is used to identify the similarity between two or more documents based on their vector representation. This helps in effective Information Retrieval
  • Text Classification
    BoW is often used to convert text into numerical features, which can then be fed into machine learning algorithms for text classification tasks such as sentiment analysis, spam detection, and topic classification
  • Feature Generation for more advanced NLP models
    BoW can be used as a preprocessing step for several NLP models such as Text Summarization, Language Models, Named Entity Recognition etc. For example: It can be used to summarize large volumes of text data by identifying and extracting the most important phrases.
  • Text Clustering
    It is used to group similar documents together based on their word frequency patterns.

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