Getting started with AllenNLP and PyTorch by training a tweet classifier

This project walks you on how to create a twitter sentiment analysis model using python. Twitter sentiment analysis is performed to identify the sentiments of the people towards various topics. For this project, we will be analysing the sentiment of people towards Pfizer vaccines. We will be using the data available on Kaggle to create this machine learning model. The collected tweets from Twitter will be analysed using machine learning to identify the different sentiments present in the tweets. The different sentiments identified in this project include positive sentiment, negative sentiment and neutral sentiment. We will also be using different classifiers to see which classifier gives the best model accuracy.

Digital Outrage Classifier from the Crockett Lab at Yale. Predicts whether tweets contain moral outrage.

Uses a Naive Bayes classifier to detect whether Trump has authored a given @RealDonaldTrump tweet.

This tool uses Word2Vec combined with Neural Networks, SVM, KNN, Naive Bayes, Decision Trees and ExtraTrees. This was used on Twitter for classifying tweets.

MLOps Implementation for Disaster Tweets Classifier Application

Tweet crawling and classification.

Contains code for a voting classifier that is part of an ensemble learning model for tweet classification (which includes an LSTM, a bayesian model and a proximity model) and a system for weighted voting

Machine Learning classifier to predict MBTI personality type of an individual using their tweets 🙍

SVM Classifier to Detect Sentiment of Tweets

Naive Bayes Tweet Sentiment Classifier in Kotlin

R shiny web application to scrape tweets based on user-defined search keyword and perform sentiment analysis of the tweets. Sentiment analysis of tweets consists of classifying tweets into emotion classes (i.e., anger, disgust, fear, joy, sadness and surprise) and also polarity classes (i.e., negative, neutral and positive) using naïve Bayes classifier. The tweets are scraped, classified into sentiment classes and visualized in R using twitteR, sentiment and ggplot2 packages, respectively.

A tweet sentiment classifier using word2vec and Keras. This Keras model can be saved and used on other tweet data, like streaming data extracted through the tweepy API.

I have used Multinomial Naive Bayes, Random Trees Embedding, Random Forest Regressor, Random Forest Classifier, Multinomial Logistic Regression, Linear Support Vector Classifier, Linear Regression, Extra Tree Regressor, Extra Tree Classifier, Decision Tree Classifier, Binary Logistic Regression and calculated accuracy score, confusion matrix and ROC(Receiver Operating Characteristic) and AUC(Area Under Curve) and finally shown how they are classifying the tweet in positive and negative.

A Python-based sentiment analysis tool that uses Scikit-Learn and a Naive Bayes classifier to predict tweet sentiment with 95% accuracy.

:skull: A Bayesian tweet classifier that can learn the difference between tweets about the YUI Library and tweets about J-pop idols named Yui. Unmaintained.

A naive bayes classifier for tweets in Node.js

Analysing Disaster related tweets dataset and build a classifier using deep learning and deploy it using Heroku

A small project that uses a Neural Network to predict when a tweet was written by Donald Trump, and when it was written by his staff.

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I qt worked on corona virus tweet streams mam With hashtags #covid19,#indiafightscorona,#lockdown I did generate the dastset from the stream and procesed according to the working of deep learning algorithms work flow. I reframed my datset with 2 parameters-- tweets full text and sentiment score and worked on 4 algorithms mam. SET 1- DEEP LEARNING ALGOTITHMS: 1.CNN -(used 1csv with train_test_split method ) Accuracy-0.73368 2.LSTM- (used 2csv file seperate for trainingand testing) Training accuracy-0.9457,loss-0.1605 Testing accuracy-0.6557,loss-0.3442 3.FFNN-( used 2csv file seperate for trainingand testing) Training accuracy-0.28,loss-622.3 Testing accuracy-0.14893,loss-141.82 4.ANN with TFIDF Vectorizer(used 1 csv wth train_test_split) The different Ann epoches and models with different learning rate and different drop out value ,Training accuracy ranged btween 0.4752 to 0.6270 and the Validation accuracy ranged 0.2353 constantly On comparing the above 4 algorithms I came to a conclusiom with my understanding Sentiment analysis in tweets can be done efficiently in this order. CNN > LSTM > ANN > FFNN. SET 2-MACHINE LEARNING I did try with Linear Support vector Classifier --1 csv train_test_split method Training accuracy - 0.6666 Testing accuracy(f1score)-0.59471 And with Naive bayes classifier--1 csv train_test_split method Training accuracy - 0.64 Test accuracy -0.5486 SET 3- MODEL CLASSIFICATIONS: I compared my datasets efficiency with 4 models . The accuracies of the model classificatiom are: 1.Baseline Model - 62.86% 2.Reduces Model-65.71% 3.Regularized Model-66.86% 4.Dropout Model-67.43% Efficient modeling order for tweet data-set Dropout model > Regularized model > Reduced model > Baseline model .

:pill: Project to build illegal drug selling tweets classifier :no_entry_sign:

This project has taken US Airlines Twitter Dataset (Training 15000 tweets & Testing 3000 tweets). It uses machine learning to classify the sentiments of tweets into positive, neutral and negative. It uses Naive-Bayes Classifier for text-classification and NLTK and SkLearn libraries in python.

Sentiment Anaylysis of Movie Reviews using Naive Bayesian & AFINN Classifier and streaming Tweets using Nodejs,Express,socket.io in backend,MongoDb as Database,AngularJs in frontend without using third-party module for authorization.

Simpel web api/service to detect fake Disaster Tweet based on ensembel machine learning model called voting classifier

Build a classifier to classify anti-national tweets from normal tweets based on the Khalistan movement.

A python project which fetches live tweets & classifies them using Naive Bayes Classifier

Sentiment Analysis of Hindi Tweets using Naive Bayes Classifier.

Dockerized basic tweet classifier app. Hate speech and offensive language detection model using various Machine Learning and NLP techniques. Also, Hate Speech Detection for tweets with k8s Cluster

A simple tweet sentiment classification using Naive Bayes Classifier