added two more experiments with features of emotion lexicons

embeddings_lexicon_features_emotion_RNN.py

+'''
+Created on 20 jun. 2018
+
+@author: fmplaza
+'''
+
+import os
+from keras.datasets.imdb import get_word_index
+from model.glove_word_embedings import GloveWordEmbednigs
+import pandas as pd
+from nltk.tokenize.casual import TweetTokenizer
+import numpy as np
+from keras.preprocessing.sequence import pad_sequences
+from keras.models import Sequential
+from keras.layers import Dense, LSTM, Embedding, Bidirectional, Conv1D, GlobalAveragePooling1D, MaxPooling1D, Dropout, Activation, Flatten
+from mpl_toolkits.axes_grid1.axes_size import Padded
+from keras.utils import np_utils
+from sklearn import metrics
+from nltk.tokenize.casual import TweetTokenizer
+from keras.preprocessing import sequence
+from keras.callbacks import EarlyStopping
+from keras.initializers import glorot_normal, glorot_uniform
+from keras import regularizers
+import random
+from tensorflow import set_random_seed
+from scipy import stats
+import csv 
+
+RANDOM_SEED = 666
+
+np.random.seed(RANDOM_SEED)
+set_random_seed(RANDOM_SEED)
+
+os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
+TWEET_TOKENIZER = TweetTokenizer(preserve_case=False, reduce_len=True, strip_handles=False)
+CLASSES = []
+EMBEDDING_DIM = 214
+
+#load GloVe model
+glove = GloveWordEmbednigs()
+glove_file = "./embeddings/glove.twitter.27B/glove.twitter.27B.200d.txt"
+glove.path_file = glove_file
+
+#Load the Glove vectors file into memory, 3 index reserved (0: paddind, 1: word not present in embedding, 2: magic word)
+number_features = 500000
+begin_ofset = 3
+glove.load(number_features, begin_ofset)
+
+
+#Load the WASSA corpus
+def read_corpus():
+    
+    classes_append = CLASSES.append
+    tweets_train_labels_numeric = []
+    tweets_dev_labels_numeric = []
+    
+    tweets_train = pd.read_csv('./corpus/train-v2.csv', sep="\t", header=0)
+    tweets_train_labels = tweets_train['emotion']
+    tweets_dev = pd.read_csv('./corpus/trial-v2.csv', sep="\t", header=0)
+    tweets_dev_labels = pd.read_csv('./corpus/trial-v2.labels', sep="\t", header=0)
+    tweets_dev_labels = tweets_dev_labels['emotion']
+    
+    
+    #convert categorical labels into numeric labels
+    for label in tweets_train_labels.tolist():
+        if(label not in CLASSES):
+            classes_append(label)
+        tweets_train_labels_numeric.append(CLASSES.index(label))
+                
+    for label in tweets_dev_labels.tolist():
+        tweets_dev_labels_numeric.append(CLASSES.index(label))       
+                
+    tweets_train_labels_numeric = np_utils.to_categorical(tweets_train_labels_numeric)
+
+    return tweets_train.tweet, tweets_train_labels_numeric, tweets_dev.tweet, tweets_dev_labels_numeric
+
+def read_lexicon():
+    
+    #read lexicon emolex
+    emolex_lexicon = {}
+    with open('lexicon/emolex_english.csv') as csvfile:
+        csvresult = csv.reader(csvfile, delimiter='\t')
+        next(csvresult)
+        for row in csvresult:
+            term = row[0]
+            anger = int(row[1])
+            disgust = int(row[2])
+            fear = int(row[3])
+            joy = int(row[4])
+            sadness = int(row[5])
+            surprise = int(row[6])           
+            trust = int(row[7])
+            anticipation = int(row[8])
+            
+            list_emotion = [term, anger, disgust, fear, joy, sadness, surprise, trust, anticipation]
+            if(term not in emolex_lexicon):
+                list_emotion = [anger, disgust, fear, joy, sadness, surprise, trust, anticipation]
+                emolex_lexicon[term] = list_emotion
+            else:
+                value = emolex_lexicon[term]
+                value[0]+=anger
+                value[1]+=disgust
+                value[2]+=fear
+                value[3]+=joy
+                value[4]+=sadness
+                value[5]+=surprise 
+                value[6]+=trust
+                value[7]+=anticipation
+    
+    #read lexicon emojis
+    emoji_lexicon = {}
+    with open('lexicon/emojis.txt') as csvfile:
+        csvresult = csv.reader(csvfile, delimiter=' ')  
+        next(csvresult)
+        for row in csvresult:
+            if(row[1] == "anger"):
+                emoji_lexicon[row[0]] = [1,0,0,0,0,0]
+            
+            if(row[1] == "disgust"):
+                emoji_lexicon[row[0]] = [0,1,0,0,0,0]
+                
+            if(row[1] == "fear"):
+                emoji_lexicon[row[0]] = [0,0,1,0,0,0]
+                
+            if(row[1] == "joy"):
+                emoji_lexicon[row[0]] = [0,0,0,1,0,0]
+                
+            if(row[1] == "sad"):
+                emoji_lexicon[row[0]] = [0,0,0,0,1,0]
+        
+            if(row[1] == "surprise"):
+                emoji_lexicon[row[0]] = [0,0,0,0,0,1]
+    
+    return emolex_lexicon, emoji_lexicon
+
+def tokenize(text):
+    #preprocessing data
+
+    text_tokenized = TWEET_TOKENIZER.tokenize(text)
+    
+    return text_tokenized
+
+
+def fit_transform_vocabulary(corpus):
+    #generate vocabulary of corpus
+    
+    #index 0: padding
+    #index 1: word not present in the embedding
+    #index 2: word magic (triggerword)
+    
+    #corpus_indexes: index of each word of tweet in the embedding model
+      
+    corpus_indexes = []
+    corpus_lengths = []
+    own_append_corpus_lengths = corpus_lengths.append
+    own_lower = str.lower
+    for doc in corpus:
+        tweet_indexes = []
+        tokens = tokenize(doc)
+        own_append_corpus_lengths(len(tokens))
+        for token in tokens:
+            if(token != "#triggerword"):
+                if(glove.is_word(own_lower(token))):
+                    word_index_embedding = glove.get_word_index(own_lower(token))
+                    tweet_indexes.append(word_index_embedding)
+                else:
+                    index = 1
+                    tweet_indexes.append(index)
+            else:
+                index = 2
+                tweet_indexes.append(index)
+
+                
+        corpus_indexes.append(tweet_indexes)
+    
+    
+    print(np.max(corpus_lengths))
+    print(np.mean(corpus_lengths))
+    print(stats.mode(corpus_lengths, axis=0))
+    return corpus_indexes
+
+
+def classification_embedings_rnn(tweets_train, tweets_train_labels_numeric, tweets_dev, emolex_lexicon, emoji_lexicon):
+    #Classification with RNN and embedings (pre-trained) 
+        
+    #calculate vocabulary    
+    corpus_train_index = fit_transform_vocabulary(tweets_train)
+    corpus_dev_index = fit_transform_vocabulary(tweets_dev)
+
+    max_len_input = 30
+               
+    train_features_pad = sequence.pad_sequences(corpus_train_index, maxlen=max_len_input, padding="post", truncating="post", value = 0)
+    padded_docs_dev = sequence.pad_sequences(corpus_dev_index, maxlen=max_len_input, padding="post", truncating="post", value = 0)
+
+    # define RNN model
+    model = Sequential()
+    
+    #assign special index
+    trigger_word_vector = 2 * 0.1 * np.random.rand(EMBEDDING_DIM) - 1
+    glove.set_embedding_vector(1, trigger_word_vector)
+    
+    vector_word_not_present = 2 * 0.1 * np.random.rand(EMBEDDING_DIM) - 1
+    glove.set_embedding_vector(2, vector_word_not_present)
+    
+            
+    #number of features in embeddings model 
+    feature_size = number_features + 3
+    embedding_matrix = np.zeros((feature_size, EMBEDDING_DIM))
+    
+    for word, idx in glove.word_indexes.items():
+        emolex_vector = [0,0,0,0,0,0,0,0]
+        emoji_vector = [0,0,0,0,0,0]
+        if(word in emolex_lexicon.keys()):
+            emolex_vector = emolex_lexicon[word]
+        if(word in emoji_lexicon.keys()):
+            emoji_vector = emoji_lexicon[word]
+        list_features_lexicons = emolex_vector + emoji_vector
+        embedding_vec = glove.get_word_embedding(word)
+        embedding_vec = np.concatenate((embedding_vec, list_features_lexicons), axis=0) 
+        if embedding_vec is not None and embedding_vec.shape[0]==EMBEDDING_DIM:
+            embedding_matrix[idx] = np.asarray(embedding_vec)
+    
+    #input_length:  Length of input sequences, when it is constant
+    e = Embedding(feature_size, EMBEDDING_DIM, input_length=max_len_input, weights=[embedding_matrix], trainable=False)
+    model.add(e)
+    #number of features:_32 each vector of 200 dim is converted to a vector of 32 dim
+    
+    model.add(LSTM(128, return_sequences=True))
+    #model.add(Bidirectional(LSTM(128, return_sequences=True)))
+    
+    model.add(Dense(64, activation='relu', kernel_initializer=glorot_uniform(seed=RANDOM_SEED), activity_regularizer=regularizers.l2(0.0001)))
+    model.add(Dropout(0.25))
+    model.add(Flatten())
+    model.add(Dense(32, activation='relu', kernel_initializer=glorot_uniform(seed=RANDOM_SEED), activity_regularizer=regularizers.l2(0.0001)))
+    model.add(Dropout(0.5))
+    model.add(Dense(len(CLASSES), activation='softmax'))
+    
+    # summarize the model
+    print(model.summary())
+
+    print("Compiling the model...")
+    # compile the model
+    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc'])
+    print("Training the model...")
+    
+    earlyStopping = EarlyStopping('loss', patience=5, mode='min')
+    
+    model.fit(train_features_pad, tweets_train_labels_numeric, batch_size=32, epochs=50, verbose=1, validation_data=(train_features_pad,tweets_train_labels_numeric), callbacks=[earlyStopping])
+    loss, accuracy = model.evaluate(train_features_pad, tweets_train_labels_numeric, batch_size=32, verbose=1)
+    print('Accuracy trainning: %f' % (accuracy*100))
+    
+    #prediction
+    tweets_dev_classified_labels = model.predict_classes(padded_docs_dev, batch_size=32, verbose=1)
+    
+    return tweets_dev_classified_labels
+    
+def calculate_quality_performamnce(y_labels, y_classified_labels, model_name):
+    
+    classes_index = [CLASSES.index(c) for c in CLASSES]
+    accruacy = metrics.accuracy_score(y_labels, y_classified_labels)
+    macro_precision = metrics.precision_score(y_labels, y_classified_labels, labels=classes_index, average="macro")
+    macro_recall = metrics.recall_score(y_labels, y_classified_labels, labels=classes_index, average="macro")
+    macro_f1 = metrics.f1_score(y_labels, y_classified_labels, labels=classes_index, average="macro")
+    
+    print("\n*** Results " + model_name + " ***")
+    print("Macro-Precision: " + str(macro_precision))
+    print("Macro-Recall: " + str(macro_recall))
+    print("Macro-F1: " + str(macro_f1))
+    print("Accuracy: " + str(accruacy))
+                          
+def main ():
+
+    tweets_train, tweets_train_labels_numeric, tweets_dev, tweets_dev_labels = read_corpus()
+    emolex_lexicon, emoji_lexicon = read_lexicon()
+    tweets_dev_classified_labels =  classification_embedings_rnn(tweets_train, tweets_train_labels_numeric, tweets_dev, emolex_lexicon, emoji_lexicon)
+    calculate_quality_performamnce(tweets_dev_labels, tweets_dev_classified_labels, "RNN_LSTM")
+
+if __name__ == '__main__':
+    main()

embeddings_lexicon_feautures_sentences_LSTM_RNN.py

+'''
+Created on 20 jun. 2018
+
+@author: fmplaza
+'''
+
+import os
+from keras.datasets.imdb import get_word_index
+from model.glove_word_embedings import GloveWordEmbednigs
+import pandas as pd
+from nltk.tokenize.casual import TweetTokenizer
+import numpy as np
+from keras.preprocessing.sequence import pad_sequences
+from keras.models import Sequential
+from keras.layers import Dense, LSTM, Embedding, Bidirectional, Conv1D, GlobalAveragePooling1D, MaxPooling1D, Dropout, Activation, Flatten
+from mpl_toolkits.axes_grid1.axes_size import Padded
+from keras.utils import np_utils
+from sklearn import metrics
+from nltk.tokenize.casual import TweetTokenizer
+from keras.preprocessing import sequence
+from keras.callbacks import EarlyStopping
+from keras.initializers import glorot_normal, glorot_uniform
+from keras import regularizers
+import random
+from tensorflow import set_random_seed
+from scipy import stats
+import csv 
+
+RANDOM_SEED = 666
+
+np.random.seed(RANDOM_SEED)
+set_random_seed(RANDOM_SEED)
+
+os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
+TWEET_TOKENIZER = TweetTokenizer(preserve_case=False, reduce_len=True, strip_handles=False)
+CLASSES = []
+EMBEDDING_DIM = 214
+
+#load GloVe model
+glove = GloveWordEmbednigs()
+glove_file = "./embeddings/glove.twitter.27B/glove.twitter.27B.200d.txt"
+glove.path_file = glove_file
+
+#Load the Glove vectors file into memory, 3 index reserved (0: paddind, 1: word not present in embedding, 2: magic word)
+number_features = 500000
+begin_ofset = 3
+glove.load(number_features, begin_ofset)
+
+
+#Load the WASSA corpus
+def read_corpus():
+    
+    classes_append = CLASSES.append
+    tweets_train_labels_numeric = []
+    tweets_dev_labels_numeric = []
+    
+    tweets_train = pd.read_csv('./corpus/train-v2.csv', sep="\t", header=0)
+    tweets_train_labels = tweets_train['emotion']
+    tweets_dev = pd.read_csv('./corpus/trial-v2.csv', sep="\t", header=0)
+    tweets_dev_labels = pd.read_csv('./corpus/trial-v2.labels', sep="\t", header=0)
+    tweets_dev_labels = tweets_dev_labels['emotion']
+    
+    
+    #convert categorical labels into numeric labels
+    for label in tweets_train_labels.tolist():
+        if(label not in CLASSES):
+            classes_append(label)
+        tweets_train_labels_numeric.append(CLASSES.index(label))
+                
+    for label in tweets_dev_labels.tolist():
+        tweets_dev_labels_numeric.append(CLASSES.index(label))       
+                
+    tweets_train_labels_numeric = np_utils.to_categorical(tweets_train_labels_numeric)
+
+    return tweets_train.tweet, tweets_train_labels_numeric, tweets_dev.tweet, tweets_dev_labels_numeric
+
+def read_lexicon():
+    
+    #read lexicon emolex
+    emolex_lexicon = {}
+    with open('lexicon/emolex_english.csv') as csvfile:
+        csvresult = csv.reader(csvfile, delimiter='\t')
+        next(csvresult)
+        for row in csvresult:
+            term = row[0]
+            anger = int(row[1])
+            disgust = int(row[2])
+            fear = int(row[3])
+            joy = int(row[4])
+            sadness = int(row[5])
+            surprise = int(row[6])           
+            trust = int(row[7])
+            anticipation = int(row[8])
+            
+            list_emotion = [term, anger, disgust, fear, joy, sadness, surprise, trust, anticipation]
+            if(term not in emolex_lexicon):
+                list_emotion = [anger, disgust, fear, joy, sadness, surprise, trust, anticipation]
+                emolex_lexicon[term] = list_emotion
+            else:
+                value = emolex_lexicon[term]
+                value[0]+=anger
+                value[1]+=disgust
+                value[2]+=fear
+                value[3]+=joy
+                value[4]+=sadness
+                value[5]+=surprise 
+                value[6]+=trust
+                value[7]+=anticipation
+    
+    #read lexicon emojis
+    emoji_lexicon = {}
+    with open('lexicon/emojis.txt') as csvfile:
+        csvresult = csv.reader(csvfile, delimiter=' ')  
+        next(csvresult)
+        for row in csvresult:
+            if(row[1] == "anger"):
+                emoji_lexicon[row[0]] = [1,0,0,0,0,0]
+            
+            if(row[1] == "disgust"):
+                emoji_lexicon[row[0]] = [0,1,0,0,0,0]
+                
+            if(row[1] == "fear"):
+                emoji_lexicon[row[0]] = [0,0,1,0,0,0]
+                
+            if(row[1] == "joy"):
+                emoji_lexicon[row[0]] = [0,0,0,1,0,0]
+                
+            if(row[1] == "sad"):
+                emoji_lexicon[row[0]] = [0,0,0,0,1,0]
+        
+            if(row[1] == "surprise"):
+                emoji_lexicon[row[0]] = [0,0,0,0,0,1]
+    
+    return emolex_lexicon, emoji_lexicon
+
+def tokenize(text):
+    #preprocessing data
+
+    text_tokenized = TWEET_TOKENIZER.tokenize(text)
+    
+    return text_tokenized
+
+
+def fit_transform_vocabulary(corpus):
+    #generate vocabulary of corpus
+    
+    #index 0: padding
+    #index 1: word not present in the embedding
+    #index 2: word magic (triggerword)
+    
+    #corpus_indexes: index of each word of tweet in the embedding model
+      
+    corpus_indexes = []
+    corpus_lengths = []
+    own_append_corpus_lengths = corpus_lengths.append
+    own_lower = str.lower
+    for doc in corpus:
+        tweet_indexes = []
+        tokens = tokenize(doc)
+        own_append_corpus_lengths(len(tokens))
+        for token in tokens:
+            if(token != "#triggerword"):
+                if(glove.is_word(own_lower(token))):
+                    word_index_embedding = glove.get_word_index(own_lower(token))
+                    tweet_indexes.append(word_index_embedding)
+                else:
+                    index = 1
+                    tweet_indexes.append(index)
+            else:
+                index = 2
+                tweet_indexes.append(index)
+
+                
+        corpus_indexes.append(tweet_indexes)
+    
+    
+    print(np.max(corpus_lengths))
+    print(np.mean(corpus_lengths))
+    print(stats.mode(corpus_lengths, axis=0))
+    return corpus_indexes
+
+
+def features_emotion_sentence(corpus, emolex_lexicon, emoji_lexicon):
+    
+    
+    features_emotion = []
+    for doc in corpus[:8]:
+        print(doc)
+        cont_word_emotion = 0
+        cont_emoji_emotion = 0
+        tokens = tokenize(doc)
+        for token in tokens:
+            token = token.lower()
+            if(token in emolex_lexicon.keys()):
+                print(token)
+                cont_word_emotion += 1
+            if(token in emoji_lexicon.keys()):
+                print(token)
+                cont_emoji_emotion += 1
+            total_count_feature_emotion = cont_word_emotion + cont_emoji_emotion
+        features_emotion.append(total_count_feature_emotion)
+    
+    return features_emotion
+                
+
+def classification_embedings_rnn(tweets_train, tweets_train_labels_numeric, tweets_dev, emolex_lexicon, emoji_lexicon):
+    #Classification with RNN and embedings (pre-trained) 
+        
+    #calculate vocabulary    
+    corpus_train_index = fit_transform_vocabulary(tweets_train)
+    corpus_dev_index = fit_transform_vocabulary(tweets_dev)
+    
+    train_features_emotion = features_emotion_sentence(tweets_train, emolex_lexicon, emoji_lexicon)
+    dev_features_emotion = features_emotion_sentence(tweets_dev, emolex_lexicon, emoji_lexicon)
+
+    max_len_input = 30
+               
+    train_features_pad = sequence.pad_sequences(corpus_train_index, maxlen=max_len_input, padding="post", truncating="post", value = 0)
+    padded_docs_dev = sequence.pad_sequences(corpus_dev_index, maxlen=max_len_input, padding="post", truncating="post", value = 0)
+
+    # define RNN model
+    model = Sequential()
+    
+    #assign special index
+    trigger_word_vector = 2 * 0.1 * np.random.rand(EMBEDDING_DIM) - 1
+    glove.set_embedding_vector(1, trigger_word_vector)
+    
+    vector_word_not_present = 2 * 0.1 * np.random.rand(EMBEDDING_DIM) - 1
+    glove.set_embedding_vector(2, vector_word_not_present)
+    
+            
+    #number of features in embeddings model 
+    feature_size = number_features + 3
+    embedding_matrix = np.zeros((feature_size, EMBEDDING_DIM))
+    
+    for word, idx in glove.word_indexes.items():
+        emolex_vector = [0,0,0,0,0,0,0,0]
+        emoji_vector = [0,0,0,0,0,0]
+        if(word in emolex_lexicon.keys()):
+            emolex_vector = emolex_lexicon[word]
+        if(word in emoji_lexicon.keys()):
+            emoji_vector = emoji_lexicon[word]
+        list_features_lexicons = emolex_vector + emoji_vector
+        embedding_vec = glove.get_word_embedding(word)
+        embedding_vec = np.concatenate((embedding_vec, list_features_lexicons), axis=0) 
+        if embedding_vec is not None and embedding_vec.shape[0]==EMBEDDING_DIM:
+            embedding_matrix[idx] = np.asarray(embedding_vec)
+    
+    #input_length:  Length of input sequences, when it is constant
+    e = Embedding(feature_size, EMBEDDING_DIM, input_length=max_len_input, weights=[embedding_matrix], trainable=False)
+    model.add(e)
+    #number of features:_32 each vector of 200 dim is converted to a vector of 32 dim
+    
+    model.add(LSTM(128, return_sequences=True))
+    #model.add(Bidirectional(LSTM(128, return_sequences=True)))
+    
+    model.add(Dense(64, activation='relu', kernel_initializer=glorot_uniform(seed=RANDOM_SEED), activity_regularizer=regularizers.l2(0.0001)))
+    model.add(Dropout(0.25))
+    model.add(Flatten())
+    model.add(Dense(32, activation='relu', kernel_initializer=glorot_uniform(seed=RANDOM_SEED), activity_regularizer=regularizers.l2(0.0001)))
+    model.add(Dropout(0.5))
+    model.add(Dense(len(CLASSES), activation='softmax'))
+    
+    # summarize the model
+    print(model.summary())
+
+    print("Compiling the model...")
+    # compile the model
+    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc'])
+    print("Training the model...")
+    
+    earlyStopping = EarlyStopping('loss', patience=5, mode='min')
+    
+    model.fit(train_features_pad, tweets_train_labels_numeric, batch_size=32, epochs=50, verbose=1, validation_data=(train_features_pad,tweets_train_labels_numeric), callbacks=[earlyStopping])
+    loss, accuracy = model.evaluate(train_features_pad, tweets_train_labels_numeric, batch_size=32, verbose=1)
+    print('Accuracy trainning: %f' % (accuracy*100))
+    
+    #prediction
+    tweets_dev_classified_labels = model.predict_classes(padded_docs_dev, batch_size=32, verbose=1)
+    
+    return tweets_dev_classified_labels
+    
+def calculate_quality_performamnce(y_labels, y_classified_labels, model_name):
+    
+    classes_index = [CLASSES.index(c) for c in CLASSES]
+    accruacy = metrics.accuracy_score(y_labels, y_classified_labels)
+    macro_precision = metrics.precision_score(y_labels, y_classified_labels, labels=classes_index, average="macro")
+    macro_recall = metrics.recall_score(y_labels, y_classified_labels, labels=classes_index, average="macro")
+    macro_f1 = metrics.f1_score(y_labels, y_classified_labels, labels=classes_index, average="macro")
+    
+    print("\n*** Results " + model_name + " ***")
+    print("Macro-Precision: " + str(macro_precision))
+    print("Macro-Recall: " + str(macro_recall))
+    print("Macro-F1: " + str(macro_f1))
+    print("Accuracy: " + str(accruacy))
+                          
+def main ():
+
+    tweets_train, tweets_train_labels_numeric, tweets_dev, tweets_dev_labels = read_corpus()
+    emolex_lexicon, emoji_lexicon = read_lexicon()
+    tweets_dev_classified_labels =  classification_embedings_rnn(tweets_train, tweets_train_labels_numeric, tweets_dev, emolex_lexicon, emoji_lexicon)
+    calculate_quality_performamnce(tweets_dev_labels, tweets_dev_classified_labels, "RNN_LSTM")
+
+if __name__ == '__main__':
+    main()

lexicon/emojis.txt

+emoji emotion
+😒 anger
+😠 anger
+😡 anger
+😣 anger
+😤 anger
+😾 anger
+👿 anger
+👊 anger
+🤮 disgust
+😨 fear
+😰 fear
+😱 fear
+😧 fear
+🙀 fear
+🆘 fear
+😁 joy
+😂 joy
+😃 joy
+😄 joy
+😅 joy
+😆 joy
+😉 joy
+😊 joy
+😋 joy
+😌 joy
+😍 joy
+😏 joy
+😘 joy
+😚 joy
+😬 joy
+😜 joy
+😝 joy
+🙃 joy
+😸 joy
+😹 joy
+😺 joy
+😻 joy
+😽 joy
+😎 joy
+🙆 joy
+🙋 joy
+🙌 joy
+❤ joy
+🧡 joy
+💛 joy
+💚 joy
+💙 joy
+💜 joy
+❣ joy
+💕 joy
+💑 joy
+💏 joy
+💋 joy
+♥ joy
+🙎 sad
+😓 sad
+😔 sad
+😖 sad
+😞 sad
+😢 sad
+😥 sad
+😩 sad
+😪 sad
+😫 sad
+😭 sad
+😿 sad
+🙍 sad
+🤒 sad
+💔 sad
+😲 surprise
+😳 surprise
+😵 surprise

results_evaluation

@@ -53,3 +53,33 @@ Macro-Precision: 0.6040684610078678
 Macro-Recall: 0.6040370418815006
 Macro-Recall: 0.6040370418815006
 Macro-F1: 0.6024329096077434
 Macro-F1: 0.6024329096077434
 Accuracy: 0.604003753518924
 Accuracy: 0.604003753518924
+
+Experiment 3: 
+
+
+vocab_embeddings = 500000
+max_lenght_tweet = 30
+
+Layers: 
+
+ e = Embedding(feature_size, EMBEDDING_DIM, input_length=max_len_input, weights=[embedding_matrix], trainable=False)
+model.add(e)
+model.add(LSTM(128, return_sequences=True))
+model.add(Dense(64, activation='relu', kernel_initializer=glorot_uniform(seed=RANDOM_SEED), activity_regularizer=regularizers.l2(0.0001)))
+model.add(Dropout(0.25))
+model.add(Flatten())
+model.add(Dense(32, activation='relu', kernel_initializer=glorot_uniform(seed=RANDOM_SEED), activity_regularizer=regularizers.l2(0.0001)))
+model.add(Dropout(0.5))
+model.add(Dense(len(CLASSES), activation='softmax'))
+
+Accuracy trainning: 84.375061
+
+Results evaluation:
+
+
+*** Results RNN_LSTM ***
+Macro-Precision: 0.552099234158447
+Macro-Recall: 0.5516921635572564
+Macro-F1: 0.550307950647363
+Accuracy: 0.5516630174121573
+