## Information
This dataset shows 1785 manually annotated tweets from German politicians during the election year 2021 (01.01.2021 - 31.12.2021).
The tweets were annotated by 6 academics which were separated into two different groups. So every group of 3 people annotated the sentiment of ~900 tweets. For every tweet, the majority label was built. The annotation result had a moderate Kappa agreement. 

## Annotation
The tweets were annotated as follows:
- 1 if the sentiment of the tweet is positive
- 2 if the sentiment of the tweet is negative
- 3 if the sentiment of the tweet is neutral
region:us
{}
TAGS
#region-us 

## Information
This dataset shows 1785 manually annotated tweets from German politicians during the election year 2021 (01.01.2021 - 31.12.2021).
The tweets were annotated by 6 academics which were separated into two different groups. So every group of 3 people annotated the sentiment of ~900 tweets. For every tweet, the majority label was built. The annotation result had a moderate Kappa agreement. 

## Annotation
The tweets were annotated as follows:
- 1 if the sentiment of the tweet is positive
- 2 if the sentiment of the tweet is negative
- 3 if the sentiment of the tweet is neutral
## Information
This dataset shows 1785 manually annotated tweets from German politicians during the election year 2021 (01.01.2021 - 31.12.2021).
The tweets were annotated by 6 academics which were separated into two different groups. So every group of 3 people annotated the sentiment of ~900 tweets. For every tweet, the majority label was built. The annotation result had a moderate Kappa agreement. ## Annotation
The tweets were annotated as follows:
- 1 if the sentiment of the tweet is positive
- 2 if the sentiment of the tweet is negative
- 3 if the sentiment of the tweet is neutral
TAGS
#region-us 
 ## Information
This dataset shows 1785 manually annotated tweets from German politicians during the election year 2021 (01.01.2021 - 31.12.2021).
The tweets were annotated by 6 academics which were separated into two different groups. So every group of 3 people annotated the sentiment of ~900 tweets. For every tweet, the majority label was built. The annotation result had a moderate Kappa agreement. ## Annotation
The tweets were annotated as follows:
- 1 if the sentiment of the tweet is positive
- 2 if the sentiment of the tweet is negative
- 3 if the sentiment of the tweet is neutral
passage: TAGS
#region-us 
## Information
This dataset shows 1785 manually annotated tweets from German politicians during the election year 2021 (01.01.2021 - 31.12.2021).
The tweets were annotated by 6 academics which were separated into two different groups. So every group of 3 people annotated the sentiment of ~900 tweets. For every tweet, the majority label was built. The annotation result had a moderate Kappa agreement.## Annotation
The tweets were annotated as follows:
- 1 if the sentiment of the tweet is positive
- 2 if the sentiment of the tweet is negative
- 3 if the sentiment of the tweet is neutral