Natural Language Processing(Part 23)-Naïve Bayes Assumptions
📚Chapter 3: Sentiment Analysis (Naive Bayes)
Description
Now I’m going to the assumptions underlying the naïve bayes method. The main one, is independence of words in a sentence. And I’ll tell you why this can be a big problem, when the method is applied. Naïve bayes is a very simple model because it doesn’t require setting any custom parameters. This method is referred to as naïve, because of the assumptions it makes about the data.The first assumption is independence between the predictors or features associated with each class. And the second, has to do with your validation set. Let’s explore each of these assumptions and how they could affect your results.
Sections
- Independence
- Naive Bayes Assumptions
Section 1- Independence
To illustrate what independence between features looks like, let’s look at the following sentence. It is sunny and hot in the Sahara desert. Naïve Bayes assumes that the words in a piece of text are independent of one another. But as you can see, this typically isn’t the case, the word sunny and hot often appear together as they do in this example. Taken together, they might also be related to the thing they’re describing, like a beach or a dessert. So the words in the sentence are not always necessarily independent of one another. But naïve bayes, assumes that they are,
Section 2- Naive Bayes Assumptions
this could lead you to under or overestimate the conditional probabilities of individual words. When using a naïve bayes, for example, if your task was to complete the sentence, it’s always cold and snowy in blank. Naïve bayes might assign equal probability to the words spring, summer, fall and winter. Even though from the context you can see that winter should be the most likely candidate And the next courses of this specialization, you will be introduced to some more sophisticated methods, let’s deal with this.
Another issue with naïve bayes is that it relies on the distribution of the training data sets. A good data set, will contain the same proportion of positive and negative tweets as a random sample. However, most of the available annotated corporal are artificially balanced, just like the data set you use for the assignment. And the real tweet stream, positive tweets tend to occur more often than their negative counterparts. One reason for this is that negative tweets, might contain content that is banned by the platform or muted by the user. Such as inappropriate or offensive vocabulary, assuming that reality behaves as your training corpus. This could result in a very optimistic or very pessimistic model. There’s a lot more on this, in the last video of this module which analyzes the sources of errors in naïve bayes.
Let’s do a quick recap of all this new information, the assumption of independence and naïve bayes is very difficult to guarantee. But despite that, the model works pretty well in certain situations. And for the assignments in this module, the relative frequency of positive and negative tweets and your training data sets, needs to be balanced in order to deliver an accurate results. Now you understand the assumptions that underlie the naïve bayes method. What if it fails to perform well, for some sentence in the next video, I’ll show you what to do in such cases.
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1- Natural Language Processing with Classification and Vector Spaces
