I aim in this project to analyze the sentiment of tweets provided from the Sentiment140 dataset by developing a machine learning sentiment analysis model involving the use of classifiers. The performance of these classifiers is then evaluated using accuracy and F1 scores.

Sentiment Analysis on Twitter data using Bernoulli Naïve Bayes

Webapp para classificar comentários (positivos, negativos e neutros) advindos do Facebook usando Natural Language Toolkit (NLTK) + Django e Bootstrap na interface Web.

NLP Project for SDAIA T5 Data Science Bootcamp. This project consists of sentiment analysis for hotel reviews and classification algorithms based on that. Also, the project has word clustering models and a hotel recpmmendation system based on the nationalities and the reviewers' scores.

Email Spam detection using python and three different algorithms.

A simple BernoulliNB spam comments detection model.

Bernoulli and Multinomial Naïve Bayes classifiers for documents using Julia

Estimating the growth or depreciation on exchange rates by using sentiment analysis method from social media comments

There are three classes InfoTheory, CompVis and Math. These can occur in any combination, so an article could be all three at once, two, one or none. The job is to build text classifiers that predict each of these three classes individually using the Abstract field.

This repo has email classifiers based on Naive Bayes classifier, Bernouilli Naive Bayes Classifier and Logistic Regression Classifier.

Machine learning Classification for Family Determination for various generations by their age, height, weight, etc...

This project aims to develop a machine learning algorithm that can accurately detect and filter out spam comments on YouTube videos.

This is a NLP - Sentiment Analysis Project built using Bernoulli-Naive-Bayes Algorithm to Predict is the IMDB Movie Review is Positive or Negative.

Spam detection using Naive Bayes Models

Sentiment analysis using Naive-Bayes Classifier

I took as input a Pokemon's many Pokedex entries and used the text to try to predict the Pokemon's type.

Predicting financial well-being through survey data from the Consumer Financial Protection Bureau

A simple Bernoulli Naive Bayes model achieved 94% accuracy on the IMDB sentiment analysis task. Despite its simplicity, it performed exceptionally well.

This project is an email classification website that determines whether an email is spam or ham (not spam) using Bernoulli and Multinomial Naive Bayes algorithms. The web application is built with Flask.

a classifier that detects spam messages using multinomial naive bayes model and a bernoulli naive bayes model