The aim of this project is to predict which tweets are about real disasters and which ones are not.

For this project I use the following methods:

• Bag of words (CountVectorizer, TfidfTransformer etc.)
• Defining a custom tokenizer that also includes lemmatization. As an alternative, nltk's TweetTokenizer.
• Classification Method 1: Multinomial Naive Bayes
• Classification Method 2: Linear Discriminant Analysis (LDA)
• TruncatedSVD along with LDA to avoid potential dimensionality problems
• Optimal hyperparameter search through GridSearchCV for LDA

Link to Kaggle competition: Addison Howard, devrishi, Phil Culliton, Yufeng Guo. (2019). Natural Language Processing with Disaster Tweets. Kaggle. https://kaggle.com/competitions/nlp-getting-started