# Classifying texts with Naive Bayes

Some weeks ago, a colleague came to ask me an idea to help him classify building constructions by their description. He have found that usually some keywords are good indicators of the building class, but there were too much data to analyse. Although I haven’t seen the data, the brief description of the problem made me think that this case could be a good application for a Naive Bayes Classifier.

In this post, I describe my intuition about the Naive Bayes Classifier and it’s mathematical formulation. Then, I present a simple example of usage of this classifier using a Python library, TextBlob.

## Naive Bayes Intuition

Our main objective here is to classify a text into different categories $c \in \mathbb{C}$ depending on its vector of words $X = [x_1, …, x_n]$. Here we already have to make a modeling decision: to consider all occurrences of each word (multinomial model) or just to mark the presence or absence of each one (Bernoulli model). Let’s take the former approach.

As we are going to construct the model from a limited amount of data (even a million of texts can be considered limited), we can expect to end up with a solution that gives us a probability of a text $d$ to belong to a class $c$: $P(c|d)$.

• It depends on the frequency of a document class. Frequent classes in our documents database may be a good indicator for the class of new documents.
• Obviously, the occurrence of words in a document can be crucial to correctly assign its class. But we don’t know exactly which words are important or how much they are.
• The occurrence of a word is probably related to the occurrence of another word in the text. But, perhaps, we can have satisfactory results even ignoring this fact (which is a naive assumption). We could assume that the occurrence of a word doesn’t depend of other words.

Those conjectures try to measure how much evidence we have in a document to classify it into a specific category, considering our known documents database. In the end, we will take the class with the highest probability, or:

## Mathematical formulation

Let’s start by expanding $P(c|d)$ using the Bayes’ theorem.

### Bayes rule and conditional probability

$P(d)$ depends on the occurrences of the words $x$; it could be calculated from our documents database and seen as a constant value. So, we could just ignore this term and try to calculate $\hat{P}(c|d)$:

where $\hat{P}$ comes from the fact that we don’t know the true values of those probabilities, but estimate than from our documents database. Now, considering the chain rule of repeated applications of conditional probability

we can derive $\hat{P}(d|c)$ as

Following our previous conjectures and assuming our naive conditional independence among the words $x$, we can write our final definition of $\hat{P}(d|c)$:

### Frequency of document classes

Returning to our target, the probability of a document $d$ being in the class $c$ given in the equation \eqref{main},

we need to calculate the frequency of the document classes $\hat{P}(c)$ from our documents database. One simple and intuitive way to do this is

where $N_c$ is the number of documents in the class $c$ and $N$ is the total number of documents in our documents database.

### Influence of each word

The last missing piece is the calculation of $\hat{P}(x_i | c)$, the probability of a word $x_i$ occurring in a document of a class $c$. The approach can be very similar to the classes’ frequency:

where $N_{x_i,c}$ is the number of occurrences of $x_i$ in the class $c$; and $N_{x,c}$ is total number of words in the class $c$. These numbers could be easily taken if we group all texts of a class in one single text blob.

However, a practical problem arises. Imagine that we are working on a text classifier for documents about sports and the classes are just different sports, like texts about basketball, soccer or baseball. Now, suppose that in our documents database there is no occurrence of the word “cancer” in a text about soccer. What happens with our classifier when it faces a new text containing the word “cancer”?

As we can see in equation \eqref{target}, we would have a multiplication involving $\hat{P}(\mbox{cancer}\,|\,\mbox{soccer})=0$, which makes the probability of the document above being in the class soccer equals to zero, even if the document is clearly a text about soccer. This seems to be called sparseness: the training dataset is never big enough to include rare events.

To solve this problem, we can use add-one smoothing, which is to assume that each word occurs at least once for each class. This idea can be translated in a modification of equation \eqref{term0}:

Now we have all elements to calculate the results of equation \eqref{target} and to train a classifier model from a document database.

## Common practices

### Bernoulli model

It’s very common to see applications of Naive Bayes using a different way to build the model. The formulation presented above is called multinomial model, where all occurrences of a term in a document are considered. In a Bernoulli model, a term is considered only as present or absent in a document, 0 or 1. Therefore, the calculation of $\hat{P}(x_i|c)$ needs to be changed to

In this equation, $N_c$ continues to be the number of documents in class $c$, but $N_{x_i, \, c}$ now is the number of documents that contains the term $x_i$. The number 2 at the denominator comes from the add-one smoothing and the fact the each term can assume only two values: presence or absence.

An important thing to note is that nonoccurring terms don’t affect the classification decision in multinomial models, but Bernoulli models model absences explicitly, using them when computing $P(c|d)$. One consequence is that usually Bernoulli models make many mistakes when classifying long documents.

### Log probabilities

As many conditional probabilities are multiplied in equation \eqref{target}, it’s reasonable to expect some problems with floating point underflow (very very small numbers to be represented by the computer). A common way to solve this problem is by applying logarithms, changing the equations \eqref{answer} and \eqref{target} into

Logarithms are usually a good choice because of their multiplication rule and monotonicity.

### Preprocessing

Usually the preprocessing step includes stemming (to reduce inflected words to their word stem or root form) and feature selection (to select a subset of terms of the training set). The purpose is to train and classify documents more efficiently by reducing the vocabulary and to avoid noise features and overfitting. Smaller the training data, more important is the preprocessing step.

There are many algorithms for stemming and feature selection, but my most seen are:

• Stemming
• Feature selection
• frequency based
• $\chi^2$

More details can be found in Manning’s book (look at References section).

## Some libraries

I have seen a lot of libraries over the web, but the most complete seems to be written in Python. The most famous is NLTK, which can be learned with the book Natural Language Processing with Python. An alternative for Java programmers is Apache OpenNLP, which is capable to support the most common NLP tasks but doesn’t have some many algorithms as NLTK. More references of books and libraries can be found here.

In addition, I always like to suggest to look for Docker images before trying to install libraries and dependencies by yourself. Images like ipython/scipystack comes with the most common scientific Python packages (NLTK is missing, but it can easily solved by this Dockerfile).

## Conclusion

In this post is presented a common problem of document classification and a possible approach to solve it by using Naive Bayes models. An informal and a more theoretical descriptions of the technique were given. In addition, some common practices and programming libraries were cited.

Many day-by-day problems are composed of repetitive (bothering) tasks, like classifying documents by their content. Fortunately, many of them can be easily and satisfactorily solved by computers and machine learning techniques, without one being necessarily an expert in any related subject.

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