How to increase the model accuracy and how to choose the number of epochs in a LSTM model from accuracy and loss curves?

Question

I am doing a NLP sentiment analysis task using an LSTM model (which currently gives me a 50% test accuracy as compared to 84% of a Naive Bayes). It is a text corpus of movie reviews from here (https://ai.stanford.edu/~amaas/data/sentiment/)

I have the following data shapes and parameters.

sent_tok_train.shape
(1400, 500)
sent_tok_test.shape
(400, 500)
sent_tok_val.shape
(200, 500)


EMBEDDING_DIM = 50 (taken from the glove embedding file, glove.6B.50d.txt)
MAXLEN = 500 
VOCAB_SIZE =  33713

DENSE1_DIM = 64
DENSE2_DIM = 32

LSTM1_DIM = 32
LSTM2_DIM = 16

WD = 0.001

Now, I have two model versions.

# Model 1
model_lstm = tf.keras.Sequential([
   tf.keras.layers.Embedding(VOCAB_SIZE+1, EMBEDDING_DIM, input_length=MAXLEN,weights=[EMBEDDINGS_MATRIX], trainable=False),
   tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(LSTM1_DIM, dropout=0.5, kernel_regularizer = regularizers.l2(WD))),
   tf.keras.layers.Dense(1, activation='sigmoid')
])

# Set the training parameters
model_lstm.compile(loss='binary_crossentropy',
                  optimizer=tf.keras.optimizers.Adam(), 
                  metrics=[tf.keras.metrics.BinaryAccuracy()])

# Model 2 - stacked
model_lstm = tf.keras.Sequential([
   tf.keras.layers.Embedding(VOCAB_SIZE+1, EMBEDDING_DIM, input_length=MAXLEN,weights=[EMBEDDINGS_MATRIX], trainable=False),
   tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(LSTM1_DIM, dropout=0.5, kernel_regularizer = regularizers.l2(WD), return_sequences=True)), 
  # tf.keras.layers.LSTM(LSTM1_DIM,dropout = 0.5, return_sequences=True),
   tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(LSTM2_DIM, dropout=0.5, kernel_regularizer = regularizers.l2(WD))),
  # tf.keras.layers.LSTM(LSTM2_DIM),
  # tf.keras.layers.Dropout(0.5),
   tf.keras.layers.Dense(DENSE1_DIM, activation='relu'),#, kernel_regularizer = regularizers.l2(WD)), 
   tf.keras.layers.Dense(DENSE2_DIM, activation='relu'),
   tf.keras.layers.Dense(1, activation='sigmoid')
])

# Set the training parameters
model_lstm.compile(loss='binary_crossentropy',
                  optimizer=tf.keras.optimizers.Adam(), 
                  metrics=[tf.keras.metrics.BinaryAccuracy()])

Q1. which one would you think is a better one, model 1 or 2?

v1. # model 1 with 350 epochs

Test loss: 1.341 Test binary accuracy: 50.50%

v2. # model 2 with 350 epochs when I fit the model, it gives slightly different results on accuracy and loss. Test loss: 1.741 Test binary accuracy: 49.75%

Q2. what would be the best number of epochs? by these pictures, it looks like around 70? because after that, the two curves bifurcate.

When I run 70 epochs, I get

#Model 1
Test loss: 1.565
Test binary accuracy: 51.50%

#Model 2:
Test loss: 0.774
Test binary accuracy: 52.75%

Q3. Is it possible to increase the validation accuracy at all by hyperparameter tuning, or changing layers, or is an LSTM just a bad model for the task above?

Thank you very much. I would be greatful for any links as to learn more about this.