your task is to develop an algorithm that determines what sort of weather the tweets reference. Specifically, the challenge is to determine whether a tweet has a negative, neutral, or positive sentiment. The following datasets are provided:
• train.csv: 22,500 tweets with the corresponding classification / sentiment. The integers 1, 2, and 3 indicate negative, neutral, and positive sentiment, respectively.
• test.csv: 7,500 tweets. Naturally, this dataset has no labels. It will be used to quantify the performance of the algorithms.
The performance of the algorithms will be then evaluated based on their capability of classifying correctly the sentiment of each tweet in the test dataset. In particular, the evaluation will be based on the accuracy metric, defined as the ratio between the number of correctly-classified samples and the total number of samples. Kaggle will calculate the value of the accuracy on two subsets of the test dataset, named public and private. The results on the public dataset will be available during the competition (public leaderboard), while the results on the private one will be available at the end of the competition (private leaderboard).
