Freaks City

This lab explores Naive Bayes features by visualizing tweet sentiment data, focusing on the log-likelihood ratio as a numeric feature for machine learning. We also introduce confidence ellipses as a tool to intuitively represent the Naive Bayes model. Imports import numpy as np import pandas as pd import matplotlib.pyplot as plt from utils impo ...

Problem Overview Online community comment systems often face the challenge of filtering offensive or inappropriate content. This implementation demonstrates how to build a text classification system using Naive Bayes to automatically identify malicious comments. The classifier categorizes input into two classes: acceptable (0) and offensive (1) ...

Reading Email Dataset The first step in our spam classification task is to load the email dataset. We'll use Python's csv module to read the SMSSpamCollection file which contains labeled SMS messages. import csv def load_sms_dataset(file_path): """ Load SMS dataset from a tab-separated file Returns: tuple of (labels, messages) ...

Spam filtering systems often utilize a bag-of-words model, where each word's frequency in a document is considered, allowing for multiple occurrences. 1. Data Preparasion: Text Segmentation Previous examples used pre-defined word vectors. Here's how to build a word list from raw text documents. Consider the following Python session: >>&gt ...