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

Finds the Happiest US and Indian State based on Sentimental Analysis of Twitter Data

Social Media Analytics: Sentiment Analysis using TextBlob, creating Word Cloud & conducting Topic Modeling using NMF from Scikit-Learn and LDA from Gensim on Twitter Tweets about Trump to study the geographic variation in opinions using Python (Anaconda 2.7).

A Django-based web app created for displaying analysis of the geographic distribution of Twitter sentiment related to COVID-19 in the UK.

This collects twitter data based on geography and keywords, filters out spam accounts, then uses the Indico API for sentiment analysis and maps the results.

What we hope to create on one portion of our proposal is an online tool using Microsoft Flow and Python. The packages I will use for building this api is tweepy. Tweepy is described as an easy-to-use python library for accessing twitter api (https://www.tweepy.org/ ). This tool will pull 100 tweets from a twitter user, place it in a csv file, and then filter and score the tweets from threatening, mild, and violent hate. After doing so, using a sentiment analysis tool, the tool will detect what score the tweet would receive in the case of the criteria made by Eve. After scoring the user, it will then do the same for their followers (limited to the first 10 so that the system does not crash). The tool will then pop up with a name of all the files that scored higher risk and place the information in a csv file. In order to create my scoring method, I will create three text files. These files will be separated by subject: non-threatening, negative, and violent text words. The csv tweet files will then be read in and filtered through these categories. They will be placed in separate arrays and then be written to a new file with only the names of the twitter user. I will be placing the text files in my github for public use and may develop a stronger tool in place of this method such as a deep learning tool so that the words may continue to become up-to-date and not rely on a static file. I will then finish with two files that have the user information placed and their score. I will work on adding context/geographical component to my sentiment analysis in order to make the data more reliable and further the study of speech detection research.

Creates a map using twitter data with sentiment analysis based on user inputed hashtag

Twitter Sentiment Analysis by Geographical Area

This project uses topic modeling and sentiment analysis methods to explore geographical narratives via the Twitter API.

This project analyzes Twitter data to explore public perceptions and behaviors related to hookah use in the US. Using Llama2-7b, alongside topic modeling and sentiment analysis, it provides insights into hookah trends, user attitudes, and geographic patterns. Findings aim to inform future regulations and tobacco prevention campaigns.

Sentiment and geographical analysis of twitter data

Sentiment and geographical analysis of twitter data

Twitter Sentiment Analysis with Geographic Visualization

Twitter sentiment and geographic context analysis

Twitter, Livestream, Sentiment Analysis, Network, Geographic mapping

Semantic, Sentiment and Geographical Analysis of low-frequency linguists on Twitter

Evaluation of the sentiments expressed in tweets about Bitcoin, a word cloud analysis to identify the most relevant keywords in the Bitcoin context, an exploration of the most used hashtags and finally a geolocation analysis to understand the geographical regions most active in the Bitcoin discourse.

App to do aggergated sentiment analysis and other text analysis using Twitter and aggregating results geographical

Using twitter sentiment analysis to measure the atittudes towards Syrian refugees in different geographical regions.

Developed a geographic visualization of Twitter data across the USA using sentiment analysis.

Renders a webpage that displays a geographic heatmap of Twitter sentiment analysis for nearby medical facilities

A twitter sentiment analysis tool that aggregates the views of people and separates them geographically, to present the general view of the public of a particular location about any policy or issue.

“A State of Mind Analyser” is based primarily on the concept of “Geotagging Based Sentiment Analysis”. “A State of Mind Analyser” is a web application, in which when inputted “Geographical Location” and “Time Period” from a given available set, displays the “overall” sentiment expressed by people on Twitter, a popular microblogging platform.

Geographical Networks Project 2020-2021. Given the current situation, from the pandemic phenomenon of Covid-19, we want to carry out a project based on Sentiment Analysis through Twitter, to understand what the Italian population thinks about it, focusing on how it reacted following the various decisions governmental (dpcm, etc.).

Twitter Data Analysis is a web application in which we gather the tweets data from Twitter using Twitter's OpenAuth API for authenticating user securely and then a variety of operations are done on the data: 1. Top 20 tweets about that topic 2. How many times that tweet has been retweeted 3. Sentiment Analysis 4. Relevant Hashtags 5. Relevant search terms 4. Number of users following a tweeter at that point of time 5. Geographical representation showing the places from which the searches are coming

This project scrapes tweets using tweepy (Twitter API) by geographic location. This is a streaming data. Further the tweets from India are considered for sentiment analysis using TextBlob. The data recieved is stored locally in a database and in a csv file which is uploaded as google sheet and thus is updated every hour using Coupler.io . Finally datawrapper is used to make choropleth map of India.

Project is to analyze people’s sentiment and topics about the new administration. Used Twitter API to collect tweets about President Trump. Conduct sentiment analysis to measure how positive or negative the collected tweets are, which can be an indirect measure of President Trump’s approval. Find what kinds of topics are discussed related to the new president, for which created word clouds and conduct topic modeling on the collected tweets. Compare the geographic variation in opinions, collected tweets from 5 different states and conducted the aforementioned three analyses. Finally, concluded the project by describing the insights gained based on the conducted analyses.

With a substantial proportion of the population currently hesitant to take the COVID-19 vaccine, it is important that people have access to accurate information. However, the bulk of the population has optimistic feelings about these vaccinations, there are also negative feelings about them, according to the analysis. In this paper, we present our dataset, a growing collection of English-language Twitter posts about COVID-19 vaccines. We provide statistics regarding the tweets over time, the hashtags used. We also illustrate how these data might be utilized by performing an analysis of the prevalence over time, topic groups of hashtags, geographical and chronologically distributions. Additionally, we develop and present a dashboard, allowing people to visualize the sentiment analysis of the COVID-19 vaccine for all Countries geolocated posts in our dataset.