It is a Natural Language Processing Problem where Sentiment Analysis is done by Classifying the Positive tweets from negative tweets by machine learning models for classification, text mining, text analysis, data analysis and data visualization

An implementation in TensorFlow of a convolutional neural network (CNN) to perform sentiment classification on tweets.

The aim of this repository is to show a baseline model for text classification by implementing a LSTM-based model coded in PyTorch. In order to provide a better understanding of the model, it will be used a Tweets dataset provided by Kaggle.

multimodal social media content (text, image) classification

Tweet Classification using RNN and CNN

End-to-end sentiment analysis of tweets using BERT. Includes preprocessing, training, and evaluation with classification reports, confusion matrices, ROC curves, and word clouds. Demonstrates fine-tuning of transformer models for text classification with modular, reproducible code.

✈ Disaster Tweet ⛴ Classification 🚀 Using NLP 🚃 Machine Learning 🛫 is a data science 🛼 that applies 🚁 Natural Language 🏘 Processing and 🛩 Machine Learning to classify tweets 🛸 as disaster related non disaster 🚟 related It demonstrates how AI ⛱ can be leveraged real time crisis management 🚢 emergency response and social media 🚝 monitoring

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Real time disaster-related tweet classification using Deep Learning.

In this project, I am trying to track the spread of disinformation. This repo is for the ML part of the project and where it tries to classify tweets as real or fake depending on the tweet text and also the text present in the article that is tagged in the tweet. This particular implementation uses BERT for classification.

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

Developing a classification model to detect hate tweets ready for deployment using various NLP techniques

Implementation of an ETL process for real-time sentiment analysis of tweets with Docker, Apache Kafka, Spark Streaming, MongoDB and Delta Lake

In this project, I extend the implementation of a Tweet/Sentence Sentiment Classification to a "Depression Assistant Chatbot". This software asks the users how they are feeling and if what they write expresses sadness of anger then they are greeted with jokes until they feel better.

NLP Project that calculates stance and classifies Arabic tweets about COVID-19 Vaccine

Corona Virus Sentiment Analysis.This challenge asks you to build a classification model to predict the sentiment of COVID-19 tweets.The tweets have been pulled from Twitter and manual tagging has been done then.

Emotion Detection & Classification of Tweets Using Streaming APIs. [NLTK] [Scikit Learn] [Twitter Streaming API] [Bing API]

Classification of twitter user's personality based on their tweets. Big Five Model used to classify the personality.

This contains my solution for the Gender-Based Violence Tweet Classification Challenge hosted on Zindi

3000 Tweet Classification

Twitter tweets play an important role in every organisation. This project is based on analysing the English tweets and categorizing the tweets based on the sentiment and emotions of the user. The literature survey conducted showed promising results of using hybrid methodologies for sentiment and emotion analysis. Four different hybrid methodologies have been used for analysing the tweets belonging to various categories. A combination of classification and regression approaches using different deep learning models such as Bidirectional LSTM, LSTM and Convolutional neural network (CNN) are implemented to perform sentiment and behaviour analysis of the tweets. A novel approach of combining Vader and NRC lexicon is used to generate the sentiment and emotion polarity and categories. The evaluation metrics such as accuracy, mean absolute error and mean square error are used to test the performance of the model. The business use cases for the models applied here can be to understand the opinion of customers towards their business to improve their service. Contradictory to the suggestions of Google’s S/W ratio method, LSTM models performed better than using CNN models for categorical as well as regression problems.

Exploring Jaccard and Cosine similarities performances then visualising their output using k means and kmeans with pca. Additional input on time series analysis, web scrapping and twitter scrapping.

A machine learning project to predict if a movie is going to be a blockbuster or flop. In this project we aim to collect data from various sources like Twitter and Youtube comments, and perform classification of postivity of these tweets and comments. Our model uses these values to predict success of a movie in the scale of 1 to 5, where 5 being blockbuster and 1 being flop. Various classification algorithms like SVM, Naive Bayes, Maximum Entropy are implemented and accuracy is compared. We uses Python as primary language of implementation.

I implement a deep learning network to classify COVID-19 Tweets into 5 categories and 3 categories using DistilBERT (a lighter version of BERT) as an embedding layer along with an LSTM and Dense Layer. I Achieve 65% accuracy with 5 categories and 80% accuracy on 3 categories.

Let us look at how we can implement text classification with Tensorflow https://www.tensorflow.org/ TensorFlow is an end-to-end open source platform for machine learning. The dataset is from the Tweet Sentiment Extraction challenge from Kaggle(https://www.kaggle.com/c/tweet-sentiment-extraction/overview) We would implement text classification using a simple convolutional network developed using Tensorflow on tweet data to classify tweets as "positive","negative" or "neutral"

A sentiment analysis tool based on Learning Sentiment- Specific Word Embedding for tweets classification

A Natural Language Processing project that performs sentiment analysis on Twitter data using TF-IDF and Logistic Regression. The model classifies tweets as positive or negative and evaluates performance using accuracy and classification report.

Predictive classification model for determining if a Tweet is discussing a disaster event (i.e., building collapse, wildfire, terrorist attack)

Resources for: Cross-Lingual Disaster-related Multi-label Tweet Classification with Manifold Mixup (ACL SRW 2020)