🤔How was your data collected and annotated? Our approach was unique because our training data was automatically created, as opposed to having humans manual annotate tweets. In our approach, we assume that any tweet with positive emoticons, like :), were positive, and tweets with negative emoticons, like :(, were negative. We used the Twitter Search API to collect these tweets by using keyword search. This is described in our paper.( https://cs.stanford.edu/people/alecmgo/papers/TwitterDistantSupervision09.pdf ). The dataset has 2 files train and test. 

Format

The data is a CSV with emoticons removed. Data file format has 6 fields:

• 0 - the polarity of the tweet (0 = negative, 2 = neutral, 4 = positive)
• 1 - the id of the tweet (2087)
• 2 - the date of the tweet (Sat May 16 23:58:44 UTC 2009)
• 3 - the query (lyx). If there is no query, then this value is NO_QUERY.
• 4 - the user that tweeted (robotickilldozr)
• 5 - the text of the tweet (Lyx is cool)