# How to improve LSTM accuracy on multiclass text classification?

So, I'm trying to build a LSTM model to classify multiclass text label. The goal is to make a prediction about user rating (1, 2, 3, 4, 5) based on their review.

My hyperparameter is like this:

# set hyperparameter
vocab_size = 5000 # make the top list of words (common words)
embedding_dim = 32
max_length = 354
oov_tok = '<OOV>' # OOV = Out of Vocabulary


Model:

model = Sequential()
model.summary()


Compile:

model.compile(
loss='categorical_crossentropy',
metrics=['accuracy']
)


Then, the result I get:

I only get 50% accuracy and I think it's not a good result. Any idea how can I improve the accuracy? Here's the collab: Collab's