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Bag of Words: Approach, Python Code, Limitations – Part III

Bag of Words: Approach, Python Code, Limitations – Part III

Posted September 25, 2020 at 9:28 am
Naman Swarnkar

Check out Part I of this series to get started with sentiment analysis. Review Part II and learn how to remove Stop Words, and use Stemming and Lemmatization

Bag of Words vs Word2Vec

Even after incorporating the mentioned techniques, it is difficult to limit the growing dimension of vectors while dealing with a large number of documents. One can indeed limit the vocabulary by limiting it to include only the most frequent words, but this results in suboptimal performance.

Word embedding models like Word2Vec results in distributed representations that take semantics into account such that words with similar meanings are present close to each other in vector space. Word2vec also limits the dimension of generated vectors. This makes Word2Vec a preferred choice for creating a vectorized representation of words.

Advantages of Bag of Words

Bag of Words is still widely used owing to its simplicity. NLP researchers usually create their first model using Bag of Words to get an idea of the performance of their work before proceeding to better word embeddings.

It is particularly helpful when we are working on a few documents and they are very domain-specific. For example: Working on Political News Data from twitter to measure sentiment. Word2Vec is a pre-trained model and thus may not have word embeddings related to niche domains.


With the help of word embedding models, one can create an end-to-end algorithmic trading pipeline for processing and leveraging alternate text data to predict potential price movements. You have seen how Bag of Words can be used to create vectorized representation. You can learn about more sophisticated techniques like Word2Vec and BERT to build sentiment analysis models in the course Natural Language Processing in Trading.

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To download the complete Python code, visit QuantInsti:

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