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.
