This dataset is collected from 1824 to 2020: 1. Year: Description: The year in which the U.S. election took place. Type: Numeric (Integer) Example: 1824, 1860, 1920, 2020

1. Candidate: Description: The name of the candidate participating in the election. Type: String (Candidate's name) Example: John Adams, Abraham Lincoln, Franklin D. Roosevelt, Joe Biden

2. Party: Description: The political party affiliation of the candidate. Type: String (Party name or abbreviation) Example: Democratic, Republican, Whig, Libertarian

3. Popular Vote: Description: The total number of votes that the candidate received in the popular vote. Type: Numeric (Integer) Example: 500,000, 5,000,000, 70,000,000

4. Result: Description: The outcome of the election for the specified candidate. Type: String (e.g., "Winner," "Runner-up," "Withdrew") Example: Winner, Runner-up, Withdrew, Conceded

5. Percentage: Description: The percentage of the total popular vote received by the candidate. Type: Numeric (Float) Example: 25.3%, 49.8%, 60.5%

This dataset appears to capture essential information about U.S. elections over time, including details about the candidates, their political party affiliations, the number of popular votes they received, the outcome of the election, and the percentage of the total popular vote they secured. This comprehensive dataset allows for the analysis of historical U.S. election trends and outcomes.

Please, If you enjoyed this dataset, don't forget to upvote it.

US Election 2020

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3244747%2F5173f21bc8eaea8335539cc942338b4d%2Fheader_win.png?generation=1605608056355359&alt=media" alt="">

Context

For this year, was the 59th quadrennial presidential election held on Tuesday, November 3, 2020. To win the election, the candidate needs 270 out of 538 electoral votes. A good sign, that show if a candidate is doing well, is if they win states that aren't expcted to go their way.

Content

This dataset contains county-level data from 2020 US Election.

Acknowledgements

• US election 2020: A really simple guide

U.S. President 1976–2020

This data file contains constituency (state-level) returns for elections to the U.S. presidency from 1976 to 2020.

For more information please refer : https://guides.lib.jjay.cuny.edu/c.php?g=992251&p=7179356

More - Find More Exciting🙀 Datasets Here - An Upvote👍 A Dayᕙ(`▿´)ᕗ , Keeps Aman Hurray Hurray..... ٩(˘◡˘)۶Haha

The County Presidential Election Returns 2000-2020 dataset describes the results of the various United States Presidential elections, broken out on a per county basis. It provides the details on the votes cast per candidate, the political party to whom the candidate belongs, and the mode by which the votes were cast. The data was collected by the MIT Election Data and Science Lab, and is housed on the Harvard Dataverse.

References: 1. https://electionlab.mit.edu/ 2. https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/VOQCHQ

Dataset

This dataset was created by Jason Yoon

Released under CC0: Public Domain

Contents

Context

The 2020 US election is happening on the 3rd November 2020 and the resulting impact to the world will no doubt be large, irrespective of which candidate is elected! After reading the two papers, here and here, I was inspired to attempt a similar sentiment analysis myself!

Content

Tweets collected, using the Twitter API statuses_lookup and snsscrape for keywords, with the original intention to try to update this dataset daily so that the timeframe will eventually cover 15.10.2020 and 04.11.2020. Added 06.11.2020 With the events of the election still ongoing as of the date that this comment was added, I've decided to keep updating the dataset with tweets until at least the end of the 6th Nov. Added 08.11.2020, just one more version pending to include tweets until at the end of the 8th Nov.

Columns are as follows: - created_at: Date and time of tweet creation - tweet_id: Unique ID of the tweet - tweet: Full tweet text - likes: Number of likes - retweet_count: Number of retweets - source: Utility used to post tweet - user_id: User ID of tweet creator - user_name: Username of tweet creator - user_screen_name: Screen name of tweet creator - user_description: Description of self by tweet creator - user_join_date: Join date of tweet creator - user_followers_count: Followers count on tweet creator - user_location: Location given on tweet creator's profile - lat: Latitude parsed from user_location - long: Longitude parsed from user_location - city: City parsed from user_location - country: Country parsed from user_location - state: State parsed from user_location - state_code: State code parsed from user_location - collected_at: Date and time tweet data was mined from twitter*

Acknowledgements

• @taniaj and her great Australian Election 2019 Tweets dataset, I used as a template for this dataset.

• Thanks to Twitter for providing the free API and snsscrape to allow collection of the tweet_ids.

Cover photo by Jorge Alcala on Unsplash Unsplash Images are distributed under a unique Unsplash License.

Inspiration

My primary interest for creating this dataset is to ascertain if there is a correlation between the sentiment of users on Twitter and the eventual election results. Other ideas that might be interesting to investigate include:

• Can we detect if there are or were any attempts at manipulating the election.
• Can we predict the candidate from tweet text only.
• Can we predict the election outcome of each state.

I also included still valid and interesting ideas from the Australian Election 2019 Tweets dataset below:

• Take into account retweets and favourites to weight overall sentiment analysis.
• Which parts of the world are interested (ie: tweet about) in the US elections, apart from the US?
• How do the users who tweet about this sort of thing tend to describe themselves?
• Is there a correlation between when the user joined Twitter and their political views (this assumes the sentiment analysis is already working well)?
• Predict gender from username/screen name and segment tweet count and sentiment by gender

Version

• Version 3 - 355,000 tweets collected, using the Twitter API statuses_lookup and snsscrape for keywords between 15.10.2020 and 22.10.2020.

• Version 5 - New tweets collected for the date of 23.10.2020, with a new total number of tweets at around 387,000 tweets.

• Version 6 - New tweets collected for the date of 24.10.2020, with a new total number of tweets at around 418,000 tweets. Additionally the "coordinates" column was removed with "lat" and "long" columns added for geolocation data (where possible).

• Version 7 - New tweets collected for the date of 25.10.2020, with a new total number of tweets at around 456,000 tweets. Added column "collected_at" to indicate when the data was mined from twitter. *Note this data is only accurate from 21.10.2020 onwards, data in the subject column before this date is an estimation.

• Version 8 - New tweets collected for the date of 26.10.2020, with a new total number of tweets at around 49...

This is the dataset I used to figure out which sociodemographic factor including the current pandemic status of each state has the most significan impace on the result of the US Presidential election last year. I also included sentiment scores of tweets created from 2020-10-15 to 2020-11-02 as well, in order to figure out the effect of positive/negative emotion for each candidate - Donald Trump and Joe Biden - on the result of the election.

Details for each variable are as below: - state: name of each state in the United States, including District of Columbia - elec16, elec20: dummy variable indicating whether Trump gained the electoral votes of each state or not. If the electors casted their votes for Trump, the value is 1; otherwise the value is 0 - elecchange: dummy variable indicating whether each party flipped the result in 2020 compared to that of the 2016 - demvote16: the rate of votes that the Democrats, i.e. Hillary Clinton earned in the 2016 Presidential election - repvote16: the rate of votes that the Republicans , i.e. Donald Trump earned in the 2016 Presidential election - demvote20: the rate of votes that the Democrats, i.e. Joe Biden earned in the 2020 Presidential election - repvote20: the rate of votes that the Republicans , i.e. Donald Trump earned in the 2020 Presidential election - demvotedif: the difference between demvote20 and demvote16 - repvotedif: the difference between repvote20 and repvote16 - pop: the population of each state - cumulcases: the cumulative COVID-19 cases on the Election day - caseMar ~ caseOct: the cumulative COVID-19 cases during each month - Marper10k ~ Octper10k: the cumulative COVID-19 cases during each month per 10 thousands - unemp20: the unemployment rate of each state this year before the election - unempdif: the difference between the unemployment rate of the last year and that of this year - jan20unemp ~ oct20unemp: the unemployment rate of each month - cumulper10k: the cumulative COVID-19 cases on the Election day per 10 thousands - b_str_poscount_total: the total number of positive tweets on Biden measured by the SentiStrength - b_str_negcount_total: the total number of negative tweets on Biden measured by the SentiStrength - t_str_poscount_total: the total number of positive tweets on Trump measured by the SentiStrength - t_str_poscount_total: the total number of negative tweets on Trump measured by the SentiStrength - b_str_posprop_total: the proportion of positive tweets on Biden measured by the SentiStrength - b_str_negprop_total: the proportion of negative tweets on Biden measured by the SentiStrength - t_str_posprop_total: the proportion of positive tweets on Trump measured by the SentiStrength - t_str_negprop_total: the proportion of negative tweets on Trump measured by the SentiStrength - white: the proportion of white people - colored: the proportion of colored people - secondary: the proportion of people who has attained the secondary education - tertiary: the proportion of people who has attained the tertiary education - q3gdp20: GDP of the 3rd quarter 2020 - q3gdprate: the growth rate of the 3rd quarter 2020, compared to that of the same quarter last year - 3qsgdp20: GDP of 3 quarters 2020 - 3qsrate20: the growth rate of GDP compared to that of the 3 quarters last year - q3gdpdif: the difference in the level of GDP of the 3rd quarter compared to the last quarter - q3rate: the growth rate of the 3rd quarter compared to the last quarter - access: the proportion of households having the Internet access

Context

The goal of this dataset is to provide a tidy way to access to the transcripts of speeches given by various US politicians in the context of the 2020 US Presidential Election. Transcripts have been scraped from rev.com. Some other information, such as location and type of speech, have been manually added to the dataset.

Content

The dataset has the following columns:

speaker: Who gave the speech

title: a title or a description of speech

text: the transcript of the speech

location: the location or the platform where the speech was give

type: type of speech (e.g., campaign speech, interview or debate)

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Comprehensive and standardized election results of winning and losing candidates for the offices of President, U.S. House, U.S. Senate, and Governor. Between 2006 and 2020 alone, there were over 4,000 races for Congress and Governor in the general election, in which 7,710 Democratic and Republican candidates ran for office. This is a candidate-level dataset that is comprehensive for Presidential, Congressional and gubernatorial contests between 2006 and 2020 with standardized names for each unique candidate, party, incumbency, vote totals, and election results.

[READ THIS FIRST! DATASETS FOR Academic/Learning/Non-commercial purpose]

Context

US Election 2020 is very interesting to look into as it is an election in the middle of a pandemic. Me and my teammate created a twitter crawler using Twitter API and Tweepy for my Artificial Intelligence coursework. We chose Donald Trump as a subject of interest as President Trump was known for his twitter interaction.

I decided to deploy my crawler on post-voting day to conduct a sentiment analysis.

Tweet text in this datasets is suitable for Sentiment Analysis usage.

Content

This raw datasets is crawled using Tweepy library and Twitter API. 2500 tweets were gathered per 15 minutes. There are total of 247,500 row of entries and 13 columns, with the total of 3,217,500 cells of data. Data cleaning is needed to perform before doing any analysis.

Datasets date range: 4th November 2020 - 11th November 2020 Tweets with "Trump", "DonalTrump", "realDonalTrump" were capture.

(The User = user of the particular row) username: Twitter User handle accDesc: Description of the user on profile location: Location of the tweet following: Total number of account the user is following followers: Total number of followers of the user totaltweets: Total tweets created of the user usercreated: Date of the user registered his/her Twitter account tweetcreated: Date of the tweet created favouritecount: tweet <3 count (equivalent to like on Facebook) retweetcount: Total tweet's retweet (equivalent to share on Facebook) text: Text body of the tweet tweetsource: Device used to create this tweet hashtags: hashtag of the tweet in JSON format

Acknowledgements and Disclaimers

Banner and thumbnail courtesy of > visuals < from unsplash.com

Much thanks to my teammate Jiacheng Loh and ChenZhen Li for the efforts.

Please do not use this datasets for any malicious attempts, any damage done is not under the responsible of me.

This datasets were gathered for the purpose of learning and not for commercial purposes.

Data were public in the public domain, therefore i assume these data is open for all.

Limitations

Datasets are gathered with at least 15 minutes interval, therefore datecreated distribution is not equal and may not include all tweets created within the date range.

This data file contains constituency (state-level) returns for elections to the U.S. Senate from 1976 to 2020.

This Dataset is collected by: MIT Election Data and Science Lab (Massachusetts Institute of Technology) (MEDSL) http://electionlab.mit.edu

Dataset

This dataset was created by Hue Dinh

Contents

There is a vast amount of literature in different disciplines, such as economics, political science, and data science, about what factors affect the prediction of election outcomes. Various data are being considered to predict the election results, such as social media posts, survey results, referendum judgments, etc.

There are various sources, such as fundamental variables, to predict election results, especially in the United States. Fundamentals refer to variables independent of the current election rhetoric, the campaign performance of a candidate immediately before an election, or social media posts. Fundamental variables include individuals' annual income, annual total family income, age, gender, marital status, race, citizenship status, language spoken at home, education level, and employment status at the individual level. Using these fundamental variables, we aim to determine whether we can predict election outcomes.

Datasets and demographic information are scraped and merged from the US Census website (https://usa.ipums.org/usa/) and MIT Election Data + Science Lab (https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/VOQCHQ).

This dataset is aimed at highlighting the potential for predicting the United States presidential election outcomes at the county level based on the fundamental variables acquired from the American Community Survey data (ACS).

Context

I like history, so figured I would compile a dataset on all US Presidential elections throughout history, and how they went.

Content

Inside are datasets for the winners, candidates, and turnout for each presidential election.

Acknowledgements

Banner Image By Gilbert Stuart https://www.clarkart.edu/artpiece/detail/george-washington, Public Domain, https://commons.wikimedia.org/w/index.php?curid=591229

Context

At the time of doing my capstone project, the US 2020 election was just around the corner and it made sense to do sentiment analysis of tweets related to the upcoming election to learn about the kind of opinions and topics being discussed in the Twitter just about 2 weeks prior to the election day. Twitter is a great source for unfiltered opinions as opposed to the typical filtered news we see from the major media outlets.

Content

439,999 tweets were collected from Twitter via Twitter API and Tweepy python package. For the details of how I collected the data, please check out my github repo where you will find my jupyter notebook with the code. https://github.com/bauyrjanj/NLP-TwitterData/blob/master/TwitterData%20-%20Problem%20Statement%20%26%20Data%20Collection.ipynb

Acknowledgements

Thanks for Twitter making it easy to collect unfiltered public opinion via their very useful Twitter API!

Inspiration

My primary interest for creating this dataset was to understand the topics of discussing by the user of Twitter and potentially identify so called October surprises that typically emerge publicly just weeks before the election day. Other ideas that might be interesting to investigate could include:

• Can we detect if there are or were any attempts to manipulate the election.
• Can we possible predict potential winner by just analyzing the tweets.
• Can we predict sentiments in each state, particularly in swing states.

This dataset contains U.S. presidential election results broken down by U.S. congressional district. It shows each party's vote percentage and overall number of votes.

Data through 2020 was scraped from the maps found here. Credit to Kiernan Park-Egan at Western Washington University. I filled in a handful of missing at-large district results so the dataset is complete.

2024 data was compiled by Drew Savicki and other contributors.

Rows per election: - 1952 — 433 (NM and ND have two at-large districts each.) - 1956 — 433 (NM and ND have two at-large districts each.) - 1960 — 435 (NM and ND have two at-large districts each. New seats in AK and HI.) - 1964 — 434 (DC gets to vote. NM and HI have two at-large districts each.) - 1968 — 435 (HI has two at-large districts.) - 1972 to present — 436

Missing data: - 1960 — 141 rows are missing Total Vote value. - 1964 — All rows are missing Total Vote value.

This data file contains constituency (state-level) returns for elections to the U.S. Senate from 1976 to 2020.

it contains the year of election, details of constituency, electoral stage ,i.e, general/runoff/primary name of winning candidate and party, total no of votes, votes obtained by the winning parties, whether candidates are write-in or not and the official/unofficial result.

Content

US Elections Transcript of US presidential debate between President Donald Trump and former vice president Joe Biden

Acknowledgements

https://www.usatoday.com https://www.usatoday.com/story/news/politics/elections/2020/10/23/debate-transcript-trump-biden-final-presidential-debate-nashville/3740152001/

US Presidential Campaign Logos

Color Diversity and Demographics

By Amber Thomas [source]

About this dataset

We conducted extensive research on popular election campaigns from 1968-2020 as compiled on Wikipedia's entry for each year. From this initial list, we excluded 32 candidates whose images could not be found--leaving us with a total of 271 primary and general party candidates across 14 electoral cycles during that period. In our search for campaign logo images, we prioritized official signs used at rallies, podiums, yards, posters, and bumper stickers with required Federal Election Commission disclaimers--resorting to using buttons only when absolutely necessary . We acknowledge that due to advances in technology, the printing process has significantly impacted the design aesthetics for modern logos compared to those made decades ago.

Using Chrome DevTools or Adobe Photoshop software programs; hexadecimal color values were retreived for each logo clipped from sources such as candidate websites or obtained through additional research efforts. To recognize RWB logos--those using only three colors of red white blue (RWB) --we also surveyed designs including accent tones paired with RWB palettes , two-color schemes (Red/Blue; Red/White; Blue/White), and multiple shades derived from a combination of any 3 primary or secondary RBW hues respectively.

In addition to visual elements associated with picture datasets , candidate demographics such as race , gender are indicated here as binary categories indicating whether a particular demographic is identifiable under one particular label ie either male / female or White / non White individuals . Candidates who fit into both these dual criteria are classfied under majority categories identified under binary labels ie ' whiteMale '. For greater census accuracy candidates classified simply as minority categorizations are merged sounding various Other labels including males belonging outidese racial definitions regardless if identifyingthemselves belonging within -- inclusion of them details belongs hereinunder :

name: The name of the candidate    (String);
party: The political party of thhe candiatate (String);              
white : Binary value indicating if thee candidiate is White     (Boolean);        male: Binary value indocating ffffthueee ccandidate is maille      (Boolean );           whitaeMaile :: Binary alula indicatig

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset can be useful for understanding trends in campaign symbolism and visual rhetoric surrounding US presidential elections over time. This data could be used to evaluate how diversity amongst candidates is reflected in their campaign visuals by looking at changes in color usage or exploring differences between Democratic and Republican campaigns.

The data can also be visualized to create charts or maps that display possible trends or themes across different elections. This can help users more easily identify patterns between campaign logs for research purposes or simply make for an interesting comparison tool to explore different aspects of certain elections through visuals rather than text alone.

Using this data is easy! Start by familiarizing yourself with all the columns included; you will find information regarding RWB & non-RWB percentages, hexadecimal value breakdowns of each logo's colors & general candidate demographic information such as gender & race. Select desired columns to focus on and decide which analysis method works best; graphical representational options including line graphs, scatter graphs & pie charts are great ways to visually explore how various factors affect color usage both within an election cycle & across multiple cycles over time! Finally you can use these insights gleaned from your analysis to generate interesting questions regarding campaign symbolism design's relationship/influence on voting population demographics/politics!

Research Ideas

• Create an interactive map to show the color trends of presidential logos over the years.
• Use a machine learning algorithm to analyze how the logo colors correlate with primary and general elections.
• Analyze how diversity and inclusion in presidential campaigns has changed by comparing RWB versus non-RWB percentages for each year or election cycle

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

...

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Fab9cfec6b3a260e778e2db84a82c0264%2Fgraph3.png?generation=1706129467014304&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F0732cc925f534209b2c1a5a6af548952%2Fgraph2.png?generation=1706129477113086&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F29ff69be3f189465e8189edc85ae5013%2Fgraph1.png?generation=1706129482734732&alt=media" alt="">

Donald Trump's Recent Favorability Ratings Among Republicans

In the aftermath of the 2020 presidential election, former President Donald Trump remains a prominent figure in American politics, especially within the Republican Party. Recent polls conducted from Jan. 4 to Jan. 23, 2024, shed light on Trump's favorability among Republicans, showcasing varying sentiments within the party.

According to a series of polls by YouGov and The Economist, conducted from Jan. 21 to Jan. 23, Trump's favorability among Republicans is notable. The first poll, with a sample size of 476, indicates an 83% favorable rating, while the second, with a larger sample size of 1,497 registered voters (RV), shows a more nuanced picture with a 46% favorable and 53% unfavorable rating. Despite some divergence in these results, the overall average favors Trump with a +68 net favorability.

Morning Consult's polls, conducted from Jan. 18 to Jan. 20 with both likely voters (LV) and RV, reveal similar trends. Among Republicans, Trump's favorability is consistently high, with an 81% favorable rating among LV and a 46% favorable rating among RV. The net favorability in both cases remains positive, at +63 and +5, respectively.

HarrisX/Harris Poll and Echelon Insights also contribute to the broader understanding of Trump's standing within the Republican base. The polls conducted from Jan. 17 to Jan. 18 by HarrisX/Harris Poll and Echelon Insights show favorable ratings of 80% and 43%, respectively, among RV and LV. However, the latter poll indicates a more balanced scenario with an even split in net favorability, while the former shows a +63 net favorability.

Moving to Ipsos and ABC News polls conducted from Jan. 16 to Jan. 17 and Jan. 10 to Jan. 17, the results remain positive for Trump. Among Republicans, Ipsos reports a 72% favorable rating, while the later poll shows a slightly lower but still substantial 66% favorable rating. Both polls indicate positive net favorability scores of +46 and +34, respectively.

The most recent set of polls by YouGov and The Economist, conducted from Jan. 14 to Jan. 16, presents a nuanced picture with favorable ratings of 84%, 45%, and 45% among Republicans. The net favorability scores range from +69 to +5, highlighting the diversity of opinions within the Republican base.

It's crucial to note the potential influence of partisan affiliations on these polls. The organizations conducting the polls vary in their partisan ties, and understanding these dynamics can provide additional context to the reported favorability ratings. As Trump continues to be a central figure in Republican politics, these polls offer a snapshot of his current standing within the party, reflecting the complexity and diversity of opinions among Republicans.