emotion detection

from keras.preprocessing.text import Tokenizer

embeddings, dim = getEmbeddings('emosense.300d.txt')
tokenizer = Tokenizer(filters='')
tokenizer.fit_on_texts([' '.join(list(embeddings.keys()))])

wordIndex = tokenizer.word_index
embeddings_matrix = getEmbeddingMatrix(wordIndex, embeddings, dim)

from keras.utils import to_categorical
from keras.preprocessing.sequence import pad_sequences
from sklearn.model_selection import train_test_split

MAX_SEQUENCE_LENGTH = 24

X_train, X_val, y_train, y_val = train_test_split(texts_train, labels_train, test_size=0.2, random_state=42)

labels_categorical_train = to_categorical(np.asarray(y_train))
labels_categorical_val = to_categorical(np.asarray(y_val))
labels_categorical_dev = to_categorical(np.asarray(labels_dev))
labels_categorical_test = to_categorical(np.asarray(labels_test))


def get_sequances(texts, sequence_length):
    message_first = pad_sequences(tokenizer.texts_to_sequences(texts[:, 0]), sequence_length)
    message_second = pad_sequences(tokenizer.texts_to_sequences(texts[:, 1]), sequence_length)
    message_third = pad_sequences(tokenizer.texts_to_sequences(texts[:, 2]), sequence_length)
    return message_first, message_second, message_third


message_first_message_train, message_second_message_train, message_third_message_train = get_sequances(X_train, MAX_SEQUENCE_LENGTH)
message_first_message_val, message_second_message_val, message_third_message_val = get_sequances(X_val, MAX_SEQUENCE_LENGTH)
message_first_message_dev, message_second_message_dev, message_third_message_dev = get_sequances(texts_dev, MAX_SEQUENCE_LENGTH)
message_first_message_test, message_second_message_test, message_third_message_test = get_sequances(texts_test, MAX_SEQUENCE_LENGTH)