CryptoSignalMonitor

This dataset encompasses a diverse collection of crypto signals derived from popular tweets by various Twitter influencers, providing insights into their impact on user behavior within the cryptocurrency space.Primary Tweets List:

Tweet Text: The textual content of the tweet.

Link: URL link to the tweet.

Author: Username of the tweet author.

Category: Categorization of the tweet content i-e general or Specific.

Date: Date of the tweet.

Number of Likes: Count of likes received by the tweet.

Value in U.S Dollar Before the Tweet: Initial value of a specified cryptocurrency before the tweet.

Value in U.S Dollar After the Tweet (Date, Open, High, Low, Close): Cryptocurrency value parameters after the tweet, including date, open, high, low, and close.

Change Status: Status indicating the change in cryptocurrency value.

% Value Before Tweet: Percentage value of the cryptocurrency before the tweet.

Change in % Value Before Tweet: Percentage change in cryptocurrency value before the tweet.

% Value After Tweet: Percentage value of the cryptocurrency after the tweet.

Trading Volume Before Tweet: Trading volume of the cryptocurrency before the tweet.

Trading Volume After Tweet: Trading volume of the cryptocurrency after the tweet.

Change Status: Status indicating the change in trading volume.

Tweet Replies List:

File #: Unique identifier for the file containing tweet replies.

Tweet Link: URL link to the original tweet.

Author: Username of the reply author.

Category: Categorization of the reply content.

IDs: Unique identifiers for the replies.

Raw Data: Original raw data of the reply.

Sentiment Analysis Results: Results of sentiment analysis for the reply.

Date: Date of the reply.

Total - Comments: Total number of negative comments.

Total + Comments: Total number of positive comments.

Total Neutral Comments: Total number of neutral comments.

Total Comments/Replies: Overall count of comments and replies to the original tweet.

This metadata provided ensures a comprehensive understanding of the dataset structure and variables, fostering transparency, and facilitating its re-use for research purposes. Researchers are encouraged to explore the dataset for insights into cryptocurrency discussions on social media.