Naive Bayes Assumptions in NLP: Understanding Independence and Data Distribution
Natural Language Processing (Part 23)
📚Chapter 3: Sentiment Analysis (Naive Bayes)
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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
One of the biggest assumptions in Naive Bayes is that features (words) are independent of each other. Let’s unpack that with a simple example.
Example Sentence:
“It is sunny and hot in the Sahara Desert.”
Naive Bayes treats each word as if it appears independently of the others. But clearly, words like “sunny” and “hot” often appear together—they're semantically related. In fact, their combination adds meaning about the environment (like a desert or beach).
This assumption of independence is rarely true in natural language. Words co-occur and depend on context. Ignoring this can lead to:
Misleading word probabilities
Incorrect predictions in sentence completion or classification tasks
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
Even though it’s a simple model, Naive Bayes makes more than one assumption. Besides independence, it also assumes that your training data reflects the real-world distribution.
But here's the catch.
Real-World vs. Training Data
Let’s say you’re classifying tweets as positive or negative. Most real tweet streams contain more positive tweets than negative ones.
However, your training dataset might be artificially balanced—with an equal number of positive and negative examples. This is common in many academic datasets and assignments.
Why This Is a Problem
If the model learns from a balanced dataset but then gets tested on real-world data (which is imbalanced), it might:
Underestimate the frequency of positive tweets
Overestimate negative sentiment
Produce biased or misleading results
This mismatch can lead to overly optimistic or overly pessimistic models depending on which class is overrepresented or underrepresented.
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.
🔁 Recap: What You Should Know About Naive Bayes Assumptions
Let’s summarize the two main assumptions in Naive Bayes:
Word Independence:
It assumes all features (like words) are independent, which is rarely the case in natural language.Training Data Distribution:
It assumes that your training data reflects the true distribution of real-world examples.
Despite these naive assumptions, the model performs reasonably well in many cases—especially when:
You have a clean and well-balanced dataset
The feature dependencies are not too strong
You need a fast, baseline model to compare with more complex approaches
🧪 What If Naive Bayes Fails?
If your Naive Bayes model performs poorly on certain examples or tasks, don’t panic. In upcoming modules or more advanced courses, you’ll be introduced to more powerful models like:
Logistic Regression
Support Vector Machines (SVM)
Recurrent Neural Networks (RNNs)
Transformers
These can handle dependencies between features and often perform better on complex NLP tasks.
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