TrainMLPRegressor.py

inputs = []
outputs = []

for tweet in tweets:
    sentence = prepareSentence(tweet["text"])
    # create our bag of words array
    bag = toBOW(sentence, final_words)
    inputs.append(bag)
    #Calculate a score, 1 if any engagement, 0 if none
    score = min(tweet["likes"] + tweet["retweets"],1)
    
    
    outputs.append(score)

#Define and train the network
nnet = MLPRegressor(activation='relu', alpha=0.0001, hidden_layer_sizes=(int(len(final_words)*0.5),int(len(final_words)*0.25)),solver='adam', max_iter=400)
nnet.fit(inputs, outputs)