Overview of the project
In this repository, sentimental analysis is done with RNN(Recurrent Neural Network) using Twitter_Data dataset which has two attributes:

• clean_text(sentences gathered from users in X(former Twitter))
  
• categories
  
  • -1: Negative
    
  • 0: Neutral
    
  • 1: Positive
    
    

------------------------------------------------------------------------------------------------

Training the model:

• To train the model, make sure that the ipynb file and csv fie both are in same location.
  
• If not, then give the directory of the dataset properly.
  
• After that, run the snippets one by one.
  
  Training the model may consume a good amount of time depending on your system. It may be around an hour.
  
  
• Once the model is trained and h5 file is saved(By default, the h5 file will be saved in the same location of the code file), there wont be any need of running the same process again.
  
• After saving the h5 file, directly run the last snippet.
  
  ------------------------------------------------------------------------------------------------
  
  Testing the model:
  
• This code allows the user to run the model and get the classification infinite number of times.
  
• Once text is entered, it gives the result if the sentence is positive/negative/neutral.
  
• It is an infinite loop and the loop can be stopped if enter is clicked without entereing any text