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.

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.

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

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

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

Sentiment analysis using Naive-Bayes Classifier

Bernoulli and Multinomial Naive Bayes classifiers are trained and tested on different datasets. We present and compare the accuracy scores for both Bernoulli NB and Multinomial NB models.

Fake News Detection** is a natural language processing task that involves identifying and classifying news articles or other types of text as real or fake.

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

The Toxic Comment Classifier using Natural Language Processing (NLP) is a project designed to detect and categorize toxic, severe toxic, identity hate, obscene, threat, and insulting comments within digital communication platforms. It may help to promote a healthier online discourse by identifying and flagging potentially harmful content.