The dataset contains approximately 2000 different (scrapped) tweets with the following attributes:
- 'id' : unique 19 digit id for each tweet
- 'created_at' : date & time of each tweet (or retweet)
- 'text' : tweet details/ description
- 'location' : origin of tweet
- Sentiment label - for each tweet based on it's text, devise a method to assign an appropriate sentiment ('positive', 'negative' or 'neutral'). This is achieved by using TextBlob (https://textblob.readthedocs.io/en/dev/)
- Text Analytics/NLP - to extract features from tweet texts
- Machine Learning - Building a robust & optimized ML model to accurately predict the sentiment associated with each tweet & explanation of the built model