:star2: :sparkles: Analyze and visualize Twitter Sentiment on a world map using Spark MLlib

Jupyter Notebook + Python code of twitter sentiment analysis

Best free, open-source datasets for data science and machine learning projects. Top government data including census, economic, financial, agricultural, image datasets, labeled and unlabeled, autonomous car datasets, and much more. Data.gov NOAA - https://www.ncdc.noaa.gov/cdo-web/ atmospheric, ocean Bureau of Labor Statistics - https://www.bls.gov/data/ employment, inflation US Census Data - https://www.census.gov/data.html demographics, income, geo, time series Bureau of Economic Analysis - http://www.bea.gov/data/gdp/gross-dom... GDP, corporate profits, savings rates Federal Reserve - https://fred.stlouisfed.org/ curency, interest rates, payroll Quandl - https://www.quandl.com/ financial and economic Data.gov.uk UK Dataservice - https://www.ukdataservice.ac.uk Census data and much more WorldBank - https://datacatalog.worldbank.org census, demographics, geographic, health, income, GDP IMF - https://www.imf.org/en/Data economic, currency, finance, commodities, time series OpenData.go.ke Kenya govt data on agriculture, education, water, health, finance, … https://data.world/ Open Data for Africa - http://dataportal.opendataforafrica.org/ agriculture, energy, environment, industry, … Kaggle - https://www.kaggle.com/datasets A huge variety of different datasets Amazon Reviews - https://snap.stanford.edu/data/web-Am... 35M product reviews from 6.6M users GroupLens - https://grouplens.org/datasets/moviel... 20M movie ratings Yelp Reviews - https://www.yelp.com/dataset 6.7M reviews, pictures, businesses IMDB Reviews - http://ai.stanford.edu/~amaas/data/se... 25k Movie reviews Twitter Sentiment 140 - http://help.sentiment140.com/for-stud... 160k Tweets Airbnb - http://insideairbnb.com/get-the-data.... A TON of data by geo UCI ML Datasets - http://mlr.cs.umass.edu/ml/ iris, wine, abalone, heart disease, poker hands, …. Enron Email dataset - http://www.cs.cmu.edu/~enron/ 500k emails from 150 people From 2001 energy scandal. See the movie: The Smartest Guys in the Room. Spambase - https://archive.ics.uci.edu/ml/datase... Emails Jeopardy Questions - https://www.reddit.com/r/datasets/com... 200k Questions and answers in json Gutenberg Ebooks - http://www.gutenberg.org/wiki/Gutenbe... Large collection of books

Daily Stock Price Movement Prediction based on Off-market Twitter Data on Daily Basis (Tweepy, Snscrape, NLP, Vader Sentiment Analysis, ML models / Top Tech Companies)

A ML/DL based Twitter sentiment analysis model based on Transformer based pretrained Encoding on sentence

AI-Driven Social Media Dashboard monitors and ingests specified tweets using stream processing and leverages a serverless architecture and ML services (Amazon Translate and Amazon Comprehend) to translate and extract insights from those tweets. The diagram below presents the architecture you can build using the example code on GitHub. AI-Driven Social Media Dashboard architecture AI-Driven Social Media Dashboard deploys an Amazon Elastic Compute Cloud (Amazon EC2) instance running in an Amazon Virtual Private Cloud (Amazon VPC) that ingests tweets from Twitter. An Amazon Kinesis Data Firehose delivery stream loads the streaming tweets into the raw prefix in the solution's Amazon Simple Storage Service (Amazon S3) bucket. Amazon S3 invokes an AWS Lambda function to analyze the raw tweets using Amazon Translate to translate non-English tweets into English, and Amazon Comprehend to use natural-language-processing (NLP) to perform entity extraction and sentiment analysis. A second Kinesis Data Firehose delivery stream loads the translated tweets and sentiment values into the sentiment prefix in the Amazon S3 bucket. A third delivery stream loads entities in the entities prefix using in the Amazon S3 bucket. The Guidance also deploys a data lake that includes AWS Glue for data transformation, Amazon Athena for data analysis, and Amazon QuickSight for data visualization. AWS Glue Data Catalog contains a logical database which is used to organize the tables for the data on Amazon S3. Athena uses these table definitions to query the data stored on Amazon S3 and return the information to an Amazon QuickSight dashboard.

PyTorch implementation of MLP and CNN for binary sentiment analysis classification using GloVe embeddings on Twitter dataset Sentiment140.

Kaggle Twitter US Airline Sentiment, Implementation of a Tweet Text Sentiment Analysis Model, using custom trained Word Embeddings and LSTM-Deep learning [TUM-Data Analysis&ML summer 2021] @adrianbruenger @stefanrmmr

The objective of this project is to detect hate speech in tweets. For the sake of simplicity, we say a tweet contains hate speech if it has a racist or sexist sentiment associated with it. So, the task is to classify racist or sexist tweets from other tweets.

Twitter Sentiment Analysis (Ml hackathon)

ML and NLTK model of Twitter Sentiment Analysis

High-accuracy Core ML model for sentiment analysis trained on a large-scale dataset. Built with Create ML to classify sentiment for tweets and general text inputs, bypassing Twitter API dependencies.

Analysis of sentiments of twitter dataset using NLP and ML

Sentiment analysis movie di twitter menggunakan database imdb_labelled.txt https://archive.ics.uci.edu/ml/datasets/Sentiment+Labelled+Sentences dan algoritma multinomial naive bayes di python 3.6.5

Automobile Accident Risk Prediction via Twitter Sentiment Analysis and Geocoding (Tweepy, ArcGIS, Smote, NLP, ML models / NYC-based)

In this deep learning project, we will train a Long Short Term Memory (LSTM) deep learning model to perform stocks sentiment analysis. Natural language processing (NLP) works by converting words (text) into numbers, these numbers are then used to train an AI/ML model to make predictions. In this project, we will build a machine learning model to analyze thousands of Twitter tweets to predict people’s sentiment towards a particular company or stock. The algorithm could be used automatically understand the sentiment from public tweets, which could be used as a factor while making buy/sell decision of securities.

Machine Learning Project: Sentiment Analysis of Movie Reviews from Tweets using Twitter API

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Extract data from Twitter API, NLP, Sentiment Analysis, ML

An ML pipeline leveraging a fine-tuned RoBERTa model to analyze NFL fan sentiment from Twitter. Includes custom web scraper, real-time analysis, and growing dataset updated monthly on Kaggle.

The project 'Sentiment Analysis of top colleges in India using Twitter Data', extracts tweets from the twitter , preprocesses them , assigns sentiment scores and then using different ML techniques , their accuracies are compared.

ML-based Cryptocurrency tracker - for Blockchain Hackathon. Sentiment analysis from Twitter and News sources to correlate and extrapolate currency exchange rates.

This repository explores sentiment and social network analysis in the context of social media platforms. Leveraging NLP techniques, including traditional ML and BERT models, it conducts sentiment analysis on a dataset of 1.6 million Twitter tweets.

An ML-based, Django app that let's counsellors manage their schedules and run sentiment analysis and topic modeling on the Twitter accounts of their clients.

I have worked on multiple projects where I implemented ML operations including Natural language processing, speech recognition, face recognition, etc. where we created projects like:- 1. Analysis of tweets from twitter and implemetion sentiment anaysis through nltk and printed results in multiple graphs.

The repository consists of a collection of Python scripts and Jupyter notebooks that guide users through the process of collecting, preprocessing, training, and evaluating ML models for sentiment analysis. By focusing on Twitter data, which is a rich source of real-time information and public opinion, the repository enables users to gain insights.

Twitter_Sentiment_Analysis_Using_ML

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Big Data analysis of Twitter data via API to analyze users' sentiment about use of AI/ML Techniques by Private Companies

Twitter Sentiment Analysis is a project aimed at analyzing the sentiment of tweets. The goal is to classify the sentiment of tweets into positive, negative, or neutral categories. This project involves preprocessing the text data, tokenizing and stemming the words, and then using machine learning models to predict the sentiment.