Machine learning & deep learning: classifying disaster tweets methods

HIGHLIGHTS

• What: The focus of this paper supports the ongoing debate on this by looking at the current methodologies used within the implementation of necessary deep learning techniques for enhanced emergency response. Stripping these characters reduces noise and helps the model focus on words rather than irrelevant symbols... tStopwords: Words like "the," "is," and "and" appear the most in text but are of little use in classification. NLTK was used to remove stopwords.By eliminating stopwords and punctuation, the model focuses on meaningful words, ultimately improving its ability to classify disaster-related tweets.. Removing stopwords helps the model . . .
  

   

  

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