# My naive (ha!) Gaussian Naive Bayes classifier is too slow

I am trying to build a film review classifier where I determine if a given review is positive or negative (w/ Python). I'm trying to avoid any other ML libraries so that I can better understand the processes. Here is my approach and the problems that I am facing:

1. I mine thousands of film reviews as training sets and classify them as positive or negative.
2. I parse through my training set and for each class, I build an array of unique words.
3. For each document, I build a vector of TF-IDF values where the vector size is my number of unique words.
4. I use a Gaussian classifier to determine: $$P(C_i|w)=P(C_i)P(w|C)=P(C_i)*\dfrac{1}{\sqrt{2\pi}\sigma_i}e^{-(1/2)(w-\mu_i)^T\sigma_i^{-1}(w-\mu_i)}$$ where $$w$$ is the my document in a vector, $$C_i$$ is a particular class, $$\mu_i$$ is the mean vector and $$\sigma_i$$ is my covariance matrix.

This approach seems to makes sense. My problem is that my algorithm is much too slow. As an example, I have sampled over 1,500 documents and I have determined over 40,000 unique words. This mean that each of my document vectors has 40,000 entries and if I were to build a covariance matrix, it would have dimensions 40,000 by 40,000. Even I were able to generate the entirety of $$\sigma_i$$, but then I would have to compute the matrix product in the exponent, which will take an extraordinarily long time just to classify one document.

I have experimented with a multinomial approach, which is working well. I very curious on how to make this work more efficiently. I realise the matrix multiplication runtime can't be improved, and I was hoping for insight on how others are able to do this.

Some things I have tried:

• Filtered any stop words (but this still leaves me with tens of thousands of words)
• Estimated $$\sigma_i$$ by summing over a couple of documents.