LSTM input and output for sentiment analysis I'm studying this LSTM network:
https://www.kaggle.com/paoloripamonti/twitter-sentiment-analysis

model = Sequential()
model.add(embedding_layer)
model.add(Dropout(0.5))
model.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2))
model.add(Dense(1, activation='sigmoid'))

model.summary()


I understand the input part of the embedding layer. Each word get's a unique vector that represents the meaning of the word.

The drop out will deactivate neurons.
So the input for the LSTM model is the vocabulary where each token is represented by a vector.

I understand the workflow in an LSTM model. But what exactly does it do with the input? Give it a score by learning?
And what is the output of the lstm?

Here is the summary:
Here it looks like the model takes in sequences of words, which are turned into embeddings, which are then put into the LSTM layer. The output of the LSTM layer is a 100 (because this is what is specified in the model) dimensional vector. This 100 dimensional vector is then put into a Dense layer outputting a 1-dimensional object, which I am assuming is some sort of prediction. Does this help? I feel like I may be missing the question.