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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
Sentiment analysis using Naive-Bayes Classifier
This is a NLP - Sentiment Analysis Project built using Bernoulli-Naive-Bayes Algorithm to Predict is the IMDB Movie Review is Positive or Negative.
Predicting financial well-being through survey data from the Consumer Financial Protection Bureau
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.
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.
Spam detection using Naive Bayes Models
In this study, after detailed Exploratory Data Analysis, 5 different machine learning models were tested on Titanic Data to answer the question "What sorts of people were more likely to survive?" and the best model for survival prediction was determined.
This project aims to develop an ML algorithm that can accurately detect and filter out spam comments on comments made on YouTube videos.
Project 1 for Machine Learning course (CS6375)
We picked up a data from Kaggle regarding musical instrument review. We applied Machine Learning algorithms to extract meaningful data that concludes sentiment of consumer.
NLP - Sentiment Analysis
This project aims to develop a machine learning algorithm that can accurately detect and filter out spam comments on YouTube videos.
Delved into advanced techniques to enhance ML performance during the uOttawa 2023 ML course. This repository offers Python implementations of Naïve Bayes (NB) and K-Nearest Neighbor (KNN) classifiers on the MCS dataset.
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.
This Python program performs sentiment analysis on movie reviews using the polarity_dataset_v2.0.
Analyzing the mood of tweets! We sort tweets on popular topics into positive, negative, or neutral categories to gauge public opinion. See what Twitter really thinks!
A Machine Learning Processing with SMS Data to predict whether the SMS is Spam/Ham with various ML Algorithms like MultinomialNB, LogisticRegression, SVC, DecisionTreeClassifier, RandomForestClassifier, KNeighborsClassifier, AdaBoostClassifier, BaggingClassifier, ExtraTreesClassifier, GradientBoostingClassifier, XGBClassifier to compare accuracy an
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.
Sentiment analysis on the IMDb dataset through a custom multivariate Bernoulli Naive Bayes implementation and a rudimentary BiGRU RNN.
a classifier that detects spam messages using multinomial naive bayes model and a bernoulli naive bayes model
Loan Eligibility Prediction Model: A machine learning application to predict loan approval based on applicant data. Includes a web interface for submitting loan applications and receiving predictions. Built with Python and Jupyter Notebook.
Spam Comments Detection using Bernoulli Naive Bayes Algorthm
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.
Machine Learning Aplicado em Classificação de Casos de Churn
We analyse sentiments of tweets which are in the form of tweets. I collected this data from Kaggle. By this strategy we can find good or bad tweets I mean which tweets are harmful and disrespectful according to Twitter guidlines.