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

Moodmap is an application which correlates data from Twitter with data from the government. Tweets are put through sentiment analysis (to assess the overall mood) and then plotted on a map according to the location from which they were tweeted from. Government data for deprivation is then overlayed on top of this.

In this assignment I am finding out peoples sentiment about DeMonetisation happened in india. Demonetization means the old unit of currency must be retired and replaced with a new currency unit. This is currently trending issue in India and people have both positive and negative sentiments about. I am collecting tweets which has #DeMonetisation using search API of twitter and classifying them as Positive and negative sentiment based on train data which will train model.In cluster.py i am finding out users who has tweeted about this issue and using there id am also Collecting whom they follow, using this information i am creating graph which will be one big component. This graph i am dividing in different communities using girvan newman. This information i am using to find out average user per community and sentiments of that community. In classify.py I am finding out positive and negative sentiments of people from tweets collected. To find out positive and negative sentiments of people, first i am training my model using train data which Is classifies using AFINN. Then i am collecting live twitter data which will be my test data, and then i am classifying this data as positive and negative sentiments. In summarize.py file i am showing final analysis. By looking at data in file we can say that more people having negative sentiments about DeMonetisation issue and people unhappy about the government decision while there are some people which are happy about government decision and having positive sentiment.

One of the major projects I have worked on till now outside of curriculum is a Twitter Government Sentiment Analysis. It is not just a regular sentiment analysis from a tweet input but has a lot more functionalities and complexity. To give a brief idea about what it does, the project searches a hashtag and displays real time tweets, the user who tweeted it, total retweet count of that tweet, all the hashtags used in each tweet, and most importantly the sentiment analysis of each tweet (whether it is a positive tweet or negative). The result shows the most recent 200 tweets from the day you want it to be searched from by taking a hashtag and date as input from the user. At the top of the result table, you get the total positive tweets percentage and negative tweets percentage of that hashtag. It is a full-fledged website with attractive frontend and smooth backend developed by me. I have developed the sentiment analysis model using logistic regression algorithm, and sqlite3 for database management. The major libraries I needed in the machine learning part are sklearn for logistic regression, nltk for preprocessing and tweepy for twitter authentication and tweets handling. I used matplotlib and seaborn libraries for result visualization to improve the accuracy of my project. The final accuracy I achieved is 98%. Coming to the website building, I have used Flask as my backend language and HTML, CSS, Javascript for frontend. Using Javascript, I was able to add beautiful scroll-animation effect to my project which gave it a more subtle and pleasing user experience. This project can be very useful for companies wanting to take a quick review on what's being said about their product on social media, especially from a specific period where they have made a significant change in their servicing or any other prospect of their product. They can understand the percentage of people who find their product/service positive or negative within seconds.

Sentiment Analysis of the Demonetization Policy of Indian Government using TwitteR and R studio

People share opinions on Twitter every second. Companies, governments, and researchers want to know what people feel about products, events, or topics in real time. Reading tweets manually is impossible at scale. SentimentFlow is a deep learning based sentiment analysis project for tweets. It trains two models like a basic RNN and an LSTM network.

🎓 Twitter web scrapper & sentiment analysis of Polish government parties

Public opinion analysis is applied in various fields in society as an increasing trend. With such analysis, decisions shall be made with a wiser and more comprehensive consideration by the government, companies, and research scholars. The goal of our project is to apply the algorithms and methodologies learned from this course to analyze the public opinions towards COVID-19 under different trending topics from Twitter and Sina Weibo, as well as conduct a comparative study between them. Our study will focus mainly on sentiment analysis. As the popular social media platforms in American and China, we believe that by analyzing the tweets and weibos, we shall conclude about how different are the trending topics in China from the other country and how the sentiment on COVID-19 different towards the topics daily.

Conducted Sentiment Analysis on online feedback regarding government policies using NLP and LSTM-BERT ensemble model using Twitter dataset and building a recommendation system to filter out relevant feedback data.

This project aims to do sentiment analysis of political views of Twitter users during the 2020 American Presidential Election campaign. This is an important study as public opinion for a particular candidate will impact the potential leader of the country. We are relying on Twitter as it acquires a large diverse data set representing the current public opinion about President Trump. The business objective of this project is to analyze the following questions: • What is the public sentiment distribution across the various locations/ states in the U.S.? • Which states require more strategic campaigning/ awareness by the Trump Government? • Does the positivity of a tweet influence the quantity of retweets/favorites? We performed the analysis to study the current state of public opinions.

The findings of the author on Sentiment Analysis of 12447 Twitter feeds in India from 10th to 21st March carrying hashtags related to Corona (#coronavirus, #COVID19, #coronaviruspandemic, #coronavirusoutbreak etc.) shows that the sentiment is polarized on the issue with no middle ground. Fortunately, majority show positive sentiments on the issue and are not panicked. We may guess that the citizens are amenable to suggestions of restrains and shall refrain from hoarding of essential food supplies and medical equipment. Such optimism may also allow us to face the economic crisis with fortitude, provided the government is able to sustain the consumer sentiment through announcements on financial aid, wage security, tax and mortgage relaxations and cut in interest rates. The scale is from negative to positive starting from 1 from 10 to 20th March 2020. It’s the time around Prime Minister’s address to the nation on Corona Virus. The mood of the people seems positive and upbeat giving hope that India as a community shall put up a disciplined and sustained fight to counter the pandemic as well as economic challenges.

This project aims to understand the sentiment when a bit policy is introduced by the government. I have used Twitter data to do sentiment analysis using R.

Applying sentiment analysis on Indonesian governments from Twitter data. At the onset of COVID-19 period between January 15 to April 15, 2020.

Sentiment analysis on Twitter tweets offers several advantages, as it provides real-time insights into public opinion, allowing businesses, governments, and researchers to monitor trends as they happen

Provides sentiment analysis and steps for making word clouds with r using tweets about Delhi Govt. obtained from Twitter. Topics include: - reading data obtained from Twitter in a csv format - cleaning tweets for further analysis - creating term document matrix - making wordcloud, lettercloud, and barplots - sentiment analysis of Delhi Government tweets before and after quarterly earnings report

Sentiment Analysis and Topic modelling is performed on Twitter data from people from state of Victoria (Australia). This analysis has been carried out to help Victorian Government to improve their public policies and management strategies. The power of harnessing people's sentiments have been utilised in various context.

System for measuring the impact of local government public policies, through twitter sentiment analysis and other artificial intelligence techniques, and for involving the people into reporting, and monitoring the implementation of such public policies.

System for measuring the impact of local government public policies, through twitter sentiment analysis and other artificial intelligence techniques, and for involving the people into reporting, and monitoring the implementation of such public policies.

This project performs sentiment analysis on Twitter data related to the controversial BBC documentary "The Modi Question" about Indian Prime Minister Narendra Modi. The documentary focuses on the 2002 Gujarat riots and the subsequent ban imposed by the Indian government.

Context The demonetization of ₹500 and ₹1000 banknotes was a step taken by the Government of India on 8 November 2016, ceasing the usage of all ₹500 and ₹1000 banknotes of the Mahatma Gandhi Series as a form of legal tender in India from 9 November 2016. The announcement was made by the Prime Minister of India Narendra Modi in an unscheduled live televised address to the nation at 20:15 Indian Standard Time (IST) the same day. In the announcement, Modi declared circulation of all ₹500 and ₹1000 banknotes of the Mahatma Gandhi Series as invalid and announced the issuance of new ₹500 and ₹2000 banknotes of the Mahatma Gandhi New Series in exchange for the old banknotes. Content The data contains 6000 most recent tweets on #demonetization. There are 6000 rows(one for each tweet) and 14 columns. Metadata: Text (Tweets) favorited favoriteCount replyToSN created truncated replyToSID id replyToUID statusSource screenName retweetCount isRetweet retweeted Acknowledgement The data was collected using the "twitteR" package in R using the twitter API. Past Research I have performed my own analysis on the data. I only did a sentiment analysis and formed a word cloud.

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.).

The following project is to create a data set on Twitter. The project is to look for tweets that are related to privacy(as in data from websites,software,etc) or trust(whether the person accepts it or not). At the same time, detect their context. The context could be related to policy, government, technology. Or it could be related to the number of retweets, followers, or tweets of the person. Then, we would relate everything through sentiment analysis(whether it’s positive or negative). The intended audience of this project is, anyone who is interested in the correlation between trust/privacy and context of the tweets, which are related to public policies, government and technology. The audience would be presented with graphs or data set to visualize the correlation.