First Name: 
Ibrahim
Last Name: 
Sabuncu
Affiliation: 
Yalova University
Job Title: 
Assistant Professor
Expertise: 
Social Media Analytics

Datasets & Analysis

This dataset includes 24,201,654 tweets related to the US Presidential Election on November 3, 2020, collected between July 1, 2020, and November 11, 2020. The related party name and sentiment scores of tweets, also the words that affect the score were added to the data set.

Instructions: 

The dataset contains more than 20 million tweets with 11 different attributes of each of them. The data file is in comma-separated values (CSV) format and its size is 3,48 GB. It is zipped by WinRAR to upload and download easily. It is zipped file size is 766 MB. It contains the following information (11 Column) for each tweet in the data file:

Created-At: Exact creation time of the tweet [Jul 1, 2020 7:44:48 PM– Nov 12, 2020 5:47:59 PM]
From-User-Id: Unique ID of the user that sent the tweet
To-User-Id: Unique ID of the user that tweet sent to
Language: Language of tweets that are coded in ISO 639-1. [%90 of tweets en: English; %3,8 und: Unidentified; %2,5 es: Spanish].
Retweet-Count: number of retweets
PartyName: The Label showing which party the tweeting is about. [Democrats] or [Republicans] if the tweet contains any keyword (that are given above) related to the Democratic or Republican party. If it contains keywords about two parties then the label is [Both]. If it doesn’t contain any keyword about two major parties (Democratic or Republican) that the label is [Neither].
Id: Unique ID of the tweet
Score: The sentiment score of the tweets. A positive (negative) score means positive (negative) emotion.
Scoring String: Nominal attribute with all words taking part in the scoring
Negativity: The sum of negative components
Positivity: The sum of positive components

The VADER algorithm is used for sentiment analysis of tweets. The VADER (Valence Aware Dictionary and sEntiment Reasoner) lexicon and rule-based sentiment algorithm to score a text. it is specifically attuned to sentiments expressed in social media and produces scores based on a dictionary of words. This operator calculates and then exposes the sum of all sentiment word scores in the text. For more details about this algorithm: https://github.com/cjhutto/vaderSentiment

This data can be used for developing election result prediction methods by social media. Also, It can be used in text mining studies such as understanding the change of feelings in tweets about parties; determining the topics that cause positive or negative feelings about the candidates; to understand the main issues that Twitter users concern about the USA election.

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This data set includes Covid-19 related Tweet messages written in Turkish that contain at least one of four keywords (Covid, Kovid, Corona, Korona). These keywords are used to express Covid-19 virus in Turkey. Tweets collection was started from 11th March 2020, the first Covid-19 case seen in Turkey.

Currently dataset contain 4,8 million tweets with 6 different attribute of each tweets that were sent from 9 March 2020 until 6 May 2020.

The data file contains comma separated values (CSV). It contains the following information (6 Column) for each tweet in the data file:

Instructions: 

Currently dataset contain 4,8 million tweets with 6 different attribute of each tweets that were sent from 9 March 2020 until 6 May 2020.

Original CSV data file is zipped by WinRAR to upload and download easily. The zipped file size is 76 MB.

This data can be used for text mining such as topic modelling, sentiment analysis etc.

The data file contains comma separated values (CSV). It contains the following information (6 Column) for each tweet in the data file:

Created-At: Exact creation time of the tweet
From-User-Id: Sender User Id
To-User-Id: if it is sent to a user, its user ID
Language: All Turkish
Retweet-Count: number of retweets
Id: ID of tweet that is unique for all tweets

Categories:
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