📊 What Else Can Naive Bayes Do? From Tweet Sentiment to Spam Filters

Natural Language Processing (Part 22)

📚Chapter 3: Sentiment Analysis (Naive Bayes)

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Introduction

Earlier in the tutorials, you used a Naive Bayes method to classify tweets. But that’s going to be used to do a number of things like identify who’s an author of a text. I’ll give you a few ideas of what those things may be.

Sections

Predict the sentiment of a tweet
Author Identity
Spam Filter
Information Retrieval
Word disambiguation

Introduction: Beyond Tweet Classification

You’ve probably seen Naive Bayes used for classifying tweets — like figuring out if a post is positive or negative. But that’s just one application. This simple and fast algorithm can handle a bunch of other text-based tasks, too. Let’s walk through a few of them.

🧠Sections 1- Predict the sentiment of a tweet

Sentiment analysis is one of the most common uses of Naive Bayes. The idea is to compute the probability of each sentiment class (like positive or negative) based on the words in the tweet.

The math behind it? Naive Bayes assumes words are independent given a class (which is rarely true in real life, but works surprisingly well). It calculates something like this:

P(class | words) ∝ P(class) * P(words | class)

Let’s try a simple example using Python and scikit-learn.

When you use Naive Bayes to predict the sentiment of a tweet, what you’re actually doing is estimating the probability for each class by using the joint probability of the words in classes. The Naive Bayes formula is as the ratio between these two probabilities, the products of the priors and the likelihoods. You can use this ratio between conditional probabilities for much more than sentiment analysis.

🧾Section 2- Author Identity

Imagine you have a stack of texts by Shakespeare and another by Hemingway. Could you tell who wrote a new paragraph? With Naive Bayes, yes.

You’d treat the author's identity as the class and calculate the probability of a document belonging to each author based on word usage. The author with the highest probability wins.

This approach is great for:

• Stylometry (identifying writing style)

• Authorship verification

• Literary analysis

For one, you could do author identification. If you had two large corporal, each written by different authors, you could train a model to recognize whether a new document was written by one or the other. Or if you had some works by Shakespeare and some works by Hemingway, you could calculate the Lambda for each word to predict how likely a new word is to be used by Shakespeare or alternatively by Hemingway. This method also allows you to determine author Identity.

🚫Section 3- Spam Filter

This one’s a classic.

Spam filters often use Naive Bayes to classify emails based on:

• Sender info

• Subject line

• Message content

The model learns what words are common in spam versus real messages and flags future emails accordingly.

Gmail? Outlook? Most email providers still rely on a version of this under the hood.

Another common use is spam filtering. Using information taken from the sender, subjects and content, you could decide whether an email is spam or not.

🔍Section 4- Information Retrieval

Before Google became what it is, Naive Bayes was used to match queries to relevant documents. The idea? Rank documents based on how likely they are to contain the query keywords.

You don’t always know what a "relevant" document looks like upfront — but you can still compute the likelihood of each one being useful. Then:

• Keep the top m documents, or

• Filter those above a certain probability threshold

Simple but effective.

One of the earliest uses of Naive Bayes was filtering between relevant and irrelevant documents in a database given the sets of keywords in a query. In this case, you only needed to calculate the likelihood of the documents given the query. You can’t know beforehand what’s a relevant or irrelevant document looks like. You can compute the likelihood for each document in your dataset and then store the documents based on its likelihoods. You can choose to keep the first m results or the ones that have a likelihood larger than a certain threshold.

🤔Section 5- Word disambiguation

Finally, let’s talk about ambiguous words — like “bank”. Is it a riverbank or a financial institution?

Naive Bayes can help disambiguate words by looking at context. For example:

• If nearby words include "money", it’s probably a financial bank.

• If you see "water" or "shore", it might refer to a riverbank.

You compare probabilities for both meanings based on context — and go with the higher score.

Finally, you can also use Naive Bayes for word disambiguation, which is to say, breaking down words for contextual clarity. Consider that you have only two possible interpretations of a given word within a text. Let’s say you don’t know if the word bank in your reading is referring to the bank of a river or to a financial institution. To disambiguate your word, calculate the score of the documents, given that it refers to each one of the possible meanings. In this case, if the texts refers to the concept of river, instead of the concept of money, then the score will be bigger than one. That’s cool, right? In summary, Bayes rule and it’s Naive approximation has a wide range of applications in sentiment analysis, author identification, information retrieval, and word disambiguation. It’s a popular method since it’s relatively simple to train use and interpret. You’ll be using the Bayes rule and Naive Bayes again in the weeks ahead. Now, you’re fully equipped. As you’ve seen in this video, Naive Bayes can be used for many classification tasks. Next, I’ll show you the assumptions that underlie the Naive Bayes method.

⚙️ Wrapping Up: Naive Bayes Is Surprisingly Versatile

You’ve seen how Naive Bayes isn’t just for basic classification tasks. It can power:

• Tweet sentiment analysis

• Author identification

• Spam filtering

• Document retrieval

• Word disambiguation

It’s fast, easy to train, and works well even with small data — making it a great baseline for NLP tasks.

🔗 Related Posts You Might Like

• Beginner’s Guide to Text Classification with Python

• How to Build a Spam Filter from Scratch

• Understanding Bayes’ Theorem with Visual Examples

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Source

1- Natural Language Processing with Classification and Vector Spaces