自然语言处理美国政客的社交媒体消息分类

数据简介: Disasters on social media

美国政客的社交媒体消息分类
内容：收集了来自美国参议员和其他美国政客的数千条社交媒体消息，可按内容分类为目标群众（国家或选民）、政治主张（中立/两党或偏见/党派）和实际内容（如攻击政敌等）

社交媒体上有些讨论是关于灾难，疾病，暴乱的，有些只是开玩笑或者是电影情节，我们该如何让机器能分辨出这两种讨论呢？

import keras
import nltk
import pandas as pd
import numpy as np
import re
import codecs

questions = pd.read_csv("socialmedia_relevant_cols_clean.csv")
questions.columns=['text', 'choose_one', 'class_label']
questions.head()

	text	choose_one	class_label
0	Just happened a terrible car crash	Relevant	1
1	Our Deeds are the Reason of this #earthquake M...	Relevant	1
2	Heard about #earthquake is different cities, s...	Relevant	1
3	there is a forest fire at spot pond, geese are...	Relevant	1
4	Forest fire near La Ronge Sask. Canada	Relevant	1

questions.describe()

	class_label
count	10876.000000
mean	0.432604
std	0.498420
min	0.000000
25%	0.000000
50%	0.000000
75%	1.000000
max	2.000000

数据清洗，去掉无用字符

def standardize_text(df, text_field):df[text_field] = df[text_field].str.replace(r"http\S+", "")df[text_field] = df[text_field].str.replace(r"http", "")df[text_field] = df[text_field].str.replace(r"@\S+", "")df[text_field] = df[text_field].str.replace(r"[^A-Za-z0-9(),!?@\'\`\"\_\n]", " ")df[text_field] = df[text_field].str.replace(r"@", "at")df[text_field] = df[text_field].str.lower()return dfquestions = standardize_text(questions, "text")questions.to_csv("clean_data.csv")
questions.head()

	text	choose_one	class_label
0	just happened a terrible car crash	Relevant	1
1	our deeds are the reason of this earthquake m...	Relevant	1
2	heard about earthquake is different cities, s...	Relevant	1
3	there is a forest fire at spot pond, geese are...	Relevant	1
4	forest fire near la ronge sask canada	Relevant	1

clean_questions = pd.read_csv("clean_data.csv")
clean_questions.tail()

	Unnamed: 0	text	choose_one	class_label
10871	10871	m1 94 01 04 utc ?5km s of volcano hawaii	Relevant	1
10872	10872	police investigating after an e bike collided ...	Relevant	1
10873	10873	the latest more homes razed by northern calif...	Relevant	1
10874	10874	meg issues hazardous weather outlook (hwo)	Relevant	1
10875	10875	cityofcalgary has activated its municipal eme...	Relevant	1

数据分布情况

数据是否倾斜

clean_questions.groupby("class_label").count()

	Unnamed: 0	text	choose_one
class_label
0	6187	6187	6187
1	4673	4673	4673
2	16	16	16

看起来还算均衡的

处理流程

分词
训练与测试集
检查与验证

from nltk.tokenize import RegexpTokenizertokenizer = RegexpTokenizer(r'\w+')clean_questions["tokens"] = clean_questions["text"].apply(tokenizer.tokenize)
clean_questions.head()

	Unnamed: 0	text	choose_one	class_label	tokens
0	0	just happened a terrible car crash	Relevant	1	[just, happened, a, terrible, car, crash]
1	1	our deeds are the reason of this earthquake m...	Relevant	1	[our, deeds, are, the, reason, of, this, earth...
2	2	heard about earthquake is different cities, s...	Relevant	1	[heard, about, earthquake, is, different, citi...
3	3	there is a forest fire at spot pond, geese are...	Relevant	1	[there, is, a, forest, fire, at, spot, pond, g...
4	4	forest fire near la ronge sask canada	Relevant	1	[forest, fire, near, la, ronge, sask, canada]

语料库情况

from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.utils import to_categoricalall_words = [word for tokens in clean_questions["tokens"] for word in tokens]
sentence_lengths = [len(tokens) for tokens in clean_questions["tokens"]]
VOCAB = sorted(list(set(all_words)))
print("%s words total, with a vocabulary size of %s" % (len(all_words), len(VOCAB)))
print("Max sentence length is %s" % max(sentence_lengths))

154724 words total, with a vocabulary size of 18101
Max sentence length is 34

句子长度情况

import matplotlib.pyplot as pltfig = plt.figure(figsize=(10, 10))
plt.xlabel('Sentence length')
plt.ylabel('Number of sentences')
plt.hist(sentence_lengths)
plt.show()

特征如何构建？

Bag of Words Counts

from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizerdef cv(data):count_vectorizer = CountVectorizer()emb = count_vectorizer.fit_transform(data)return emb, count_vectorizerlist_corpus = clean_questions["text"].tolist()
list_labels = clean_questions["class_label"].tolist()X_train, X_test, y_train, y_test = train_test_split(list_corpus, list_labels, test_size=0.2, random_state=40)X_train_counts, count_vectorizer = cv(X_train)
X_test_counts = count_vectorizer.transform(X_test)

PCA展示Bag of Words

from sklearn.decomposition import PCA, TruncatedSVD
import matplotlib
import matplotlib.patches as mpatchesdef plot_LSA(test_data, test_labels, savepath="PCA_demo.csv", plot=True):lsa = TruncatedSVD(n_components=2)lsa.fit(test_data)lsa_scores = lsa.transform(test_data)color_mapper = {label:idx for idx,label in enumerate(set(test_labels))}color_column = [color_mapper[label] for label in test_labels]colors = ['orange','blue','blue']if plot:plt.scatter(lsa_scores[:,0], lsa_scores[:,1], s=8, alpha=.8, c=test_labels, cmap=matplotlib.colors.ListedColormap(colors))red_patch = mpatches.Patch(color='orange', label='Irrelevant')green_patch = mpatches.Patch(color='blue', label='Disaster')plt.legend(handles=[red_patch, green_patch], prop={'size': 30})fig = plt.figure(figsize=(16, 16))
plot_LSA(X_train_counts, y_train)
plt.show()

看起来并没有将这两类点区分开

逻辑回归看一下结果

from sklearn.linear_model import LogisticRegressionclf = LogisticRegression(C=30.0, class_weight='balanced', solver='newton-cg', multi_class='multinomial', n_jobs=-1, random_state=40)
clf.fit(X_train_counts, y_train)y_predicted_counts = clf.predict(X_test_counts)

评估

from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_reportdef get_metrics(y_test, y_predicted):  # true positives / (true positives+false positives)precision = precision_score(y_test, y_predicted, pos_label=None,average='weighted')             # true positives / (true positives + false negatives)recall = recall_score(y_test, y_predicted, pos_label=None,average='weighted')# harmonic mean of precision and recallf1 = f1_score(y_test, y_predicted, pos_label=None, average='weighted')# true positives + true negatives/ totalaccuracy = accuracy_score(y_test, y_predicted)return accuracy, precision, recall, f1accuracy, precision, recall, f1 = get_metrics(y_test, y_predicted_counts)
print("accuracy = %.3f, precision = %.3f, recall = %.3f, f1 = %.3f" % (accuracy, precision, recall, f1))

accuracy = 0.754, precision = 0.752, recall = 0.754, f1 = 0.753

混淆矩阵检查

import numpy as np
import itertools
from sklearn.metrics import confusion_matrixdef plot_confusion_matrix(cm, classes,normalize=False,title='Confusion matrix',cmap=plt.cm.winter):if normalize:cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]plt.imshow(cm, interpolation='nearest', cmap=cmap)plt.title(title, fontsize=30)plt.colorbar()tick_marks = np.arange(len(classes))plt.xticks(tick_marks, classes, fontsize=20)plt.yticks(tick_marks, classes, fontsize=20)fmt = '.2f' if normalize else 'd'thresh = cm.max() / 2.for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):plt.text(j, i, format(cm[i, j], fmt), horizontalalignment="center", color="white" if cm[i, j] < thresh else "black", fontsize=40)plt.tight_layout()plt.ylabel('True label', fontsize=30)plt.xlabel('Predicted label', fontsize=30)return plt

cm = confusion_matrix(y_test, y_predicted_counts)
fig = plt.figure(figsize=(10, 10))
plot = plot_confusion_matrix(cm, classes=['Irrelevant','Disaster','Unsure'], normalize=False, title='Confusion matrix')
plt.show()
print(cm)

[[970 251   3][274 670   1][  3   4   0]]

第三类咋没有一个呢。。。因为数据里面就没几个啊。。。

进一步检查模型的关注点

def get_most_important_features(vectorizer, model, n=5):index_to_word = {v:k for k,v in vectorizer.vocabulary_.items()}# loop for each classclasses ={}for class_index in range(model.coef_.shape[0]):word_importances = [(el, index_to_word[i]) for i,el in enumerate(model.coef_[class_index])]sorted_coeff = sorted(word_importances, key = lambda x : x[0], reverse=True)tops = sorted(sorted_coeff[:n], key = lambda x : x[0])bottom = sorted_coeff[-n:]classes[class_index] = {'tops':tops,'bottom':bottom}return classesimportance = get_most_important_features(count_vectorizer, clf, 10)

def plot_important_words(top_scores, top_words, bottom_scores, bottom_words, name):y_pos = np.arange(len(top_words))top_pairs = [(a,b) for a,b in zip(top_words, top_scores)]top_pairs = sorted(top_pairs, key=lambda x: x[1])bottom_pairs = [(a,b) for a,b in zip(bottom_words, bottom_scores)]bottom_pairs = sorted(bottom_pairs, key=lambda x: x[1], reverse=True)top_words = [a[0] for a in top_pairs]top_scores = [a[1] for a in top_pairs]bottom_words = [a[0] for a in bottom_pairs]bottom_scores = [a[1] for a in bottom_pairs]fig = plt.figure(figsize=(10, 10))  plt.subplot(121)plt.barh(y_pos,bottom_scores, align='center', alpha=0.5)plt.title('Irrelevant', fontsize=20)plt.yticks(y_pos, bottom_words, fontsize=14)plt.suptitle('Key words', fontsize=16)plt.xlabel('Importance', fontsize=20)plt.subplot(122)plt.barh(y_pos,top_scores, align='center', alpha=0.5)plt.title('Disaster', fontsize=20)plt.yticks(y_pos, top_words, fontsize=14)plt.suptitle(name, fontsize=16)plt.xlabel('Importance', fontsize=20)plt.subplots_adjust(wspace=0.8)plt.show()top_scores = [a[0] for a in importance[1]['tops']]
top_words = [a[1] for a in importance[1]['tops']]
bottom_scores = [a[0] for a in importance[1]['bottom']]
bottom_words = [a[1] for a in importance[1]['bottom']]plot_important_words(top_scores, top_words, bottom_scores, bottom_words, "Most important words for relevance")

我们的模型找到了一些模式，但是看起来还不够好

TFIDF Bag of Words

这样我们就不均等对待每一个词了

def tfidf(data):tfidf_vectorizer = TfidfVectorizer()train = tfidf_vectorizer.fit_transform(data)return train, tfidf_vectorizerX_train_tfidf, tfidf_vectorizer = tfidf(X_train)
X_test_tfidf = tfidf_vectorizer.transform(X_test)

fig = plt.figure(figsize=(16, 16))
plot_LSA(X_train_tfidf, y_train)
plt.show()

看起来好那么一丁丁丁丁点

clf_tfidf = LogisticRegression(C=30.0, class_weight='balanced', solver='newton-cg', multi_class='multinomial', n_jobs=-1, random_state=40)
clf_tfidf.fit(X_train_tfidf, y_train)y_predicted_tfidf = clf_tfidf.predict(X_test_tfidf)

accuracy_tfidf, precision_tfidf, recall_tfidf, f1_tfidf = get_metrics(y_test, y_predicted_tfidf)
print("accuracy = %.3f, precision = %.3f, recall = %.3f, f1 = %.3f" % (accuracy_tfidf, precision_tfidf, recall_tfidf, f1_tfidf))

accuracy = 0.762, precision = 0.760, recall = 0.762, f1 = 0.761

cm2 = confusion_matrix(y_test, y_predicted_tfidf)
fig = plt.figure(figsize=(10, 10))
plot = plot_confusion_matrix(cm2, classes=['Irrelevant','Disaster','Unsure'], normalize=False, title='Confusion matrix')
plt.show()
print("TFIDF confusion matrix")
print(cm2)
print("BoW confusion matrix")
print(cm)

TFIDF confusion matrix
[[974 249   1][261 684   0][  3   4   0]]
BoW confusion matrix
[[970 251   3][274 670   1][  3   4   0]]

词语的解释

importance_tfidf = get_most_important_features(tfidf_vectorizer, clf_tfidf, 10)

top_scores = [a[0] for a in importance_tfidf[1]['tops']]
top_words = [a[1] for a in importance_tfidf[1]['tops']]
bottom_scores = [a[0] for a in importance_tfidf[1]['bottom']]
bottom_words = [a[1] for a in importance_tfidf[1]['bottom']]plot_important_words(top_scores, top_words, bottom_scores, bottom_words, "Most important words for relevance")

这些词看起来比之前强一些了

问题

我们现在考虑的是每一个词基于频率的情况，如果在新的测试环境下有些词变了呢？比如说goog和positive.有些词可能表达的意义差不多但是却长得不一样，这样我们的模型就难捕捉到了。

word2vec

一句话解释：比较牛逼。。。

import gensimword2vec_path = "GoogleNews-vectors-negative300.bin"
word2vec = gensim.models.KeyedVectors.load_word2vec_format(word2vec_path, binary=True)

def get_average_word2vec(tokens_list, vector, generate_missing=False, k=300):if len(tokens_list)<1:return np.zeros(k)if generate_missing:vectorized = [vector[word] if word in vector else np.random.rand(k) for word in tokens_list]else:vectorized = [vector[word] if word in vector else np.zeros(k) for word in tokens_list]length = len(vectorized)summed = np.sum(vectorized, axis=0)averaged = np.divide(summed, length)return averageddef get_word2vec_embeddings(vectors, clean_questions, generate_missing=False):embeddings = clean_questions['tokens'].apply(lambda x: get_average_word2vec(x, vectors, generate_missing=generate_missing))return list(embeddings)

embeddings = get_word2vec_embeddings(word2vec, clean_questions)
X_train_word2vec, X_test_word2vec, y_train_word2vec, y_test_word2vec = train_test_split(embeddings, list_labels, test_size=0.2, random_state=40)

X_train_word2vec[0]

array([ 0.05639939,  0.02053833,  0.07635207,  0.06914993, -0.01007262,-0.04978943,  0.02546038, -0.06045968,  0.04264323,  0.02419935,0.00375076, -0.15124639,  0.02915809, -0.01554943, -0.10182699,0.05523972,  0.00953747,  0.0834525 ,  0.00200544, -0.0238909 ,-0.01706369,  0.09193638,  0.03979783,  0.04899052,  0.04707618,-0.09235491, -0.10698809,  0.07503255,  0.04905628, -0.01991781,0.04036749, -0.0117856 , -0.00576346,  0.01624843, -0.01823952,-0.01545715,  0.06020392,  0.02975609,  0.02211217,  0.07844525,0.05023847, -0.09430913,  0.20582217, -0.05274091,  0.00881231,0.04394059, -0.01748512, -0.0403268 ,  0.03178769,  0.06038993,0.03867458,  0.00492932,  0.05121649,  0.01256743, -0.02096994,0.02814593, -0.06389218,  0.01661319, -0.02686709, -0.07981364,-0.00288318,  0.07032367, -0.07524182, -0.01155599, -0.0259661 ,0.00625901, -0.05474758, -0.00059877, -0.01737177,  0.07586161,0.0273136 , -0.00077093,  0.0752638 ,  0.05861119, -0.15668742,-0.00779506,  0.04997617,  0.08768209,  0.04078311,  0.07749503,0.02886018, -0.08094715,  0.05818976, -0.02744593, -0.00559489,-0.00488863, -0.06092762,  0.15089634, -0.02423968,  0.02867635,0.0041097 ,  0.00409226, -0.05106317, -0.0156715 , -0.06731596,0.00594657,  0.02464658,  0.10740153,  0.0207287 , -0.02535357,-0.05631002, -0.01714507, -0.04964483, -0.00834728, -0.01148841,0.04122198,  0.00281052, -0.02053833,  0.01521229, -0.10191563,-0.07321421, -0.01803589, -0.02788144,  0.00172424,  0.07978603,-0.01517505,  0.03893743, -0.0548212 ,  0.03782436,  0.04642305,-0.05222284,  0.01304263, -0.06944965,  0.01763625, -0.02670433,-0.03698331, -0.02478899, -0.06544131,  0.05864679, -0.00175549,-0.11564055, -0.10066441, -0.04190209, -0.02992467, -0.08564534,-0.02061244,  0.02688017, -0.0045171 ,  0.00165086,  0.10750544,-0.028361  , -0.03209577,  0.0515936 , -0.04164342,  0.02281843,0.08524286, -0.10112653, -0.14161319, -0.05427769, -0.01017171,0.09955125,  0.02694847, -0.0915055 ,  0.09549531, -0.0138172 ,0.01547096,  0.00868443, -0.04557078, -0.00442069,  0.01043919,-0.00775728,  0.02804129,  0.10577102,  0.07417879, -0.0414545 ,-0.10446894,  0.07996532, -0.06722441,  0.0636742 , -0.05054583,-0.11369978,  0.02922131, -0.03643508, -0.09067681, -0.06278338,-0.01135545,  0.09446498, -0.02156576,  0.00918143,  0.0722787 ,-0.01088969,  0.03180022, -0.00304031,  0.0532895 ,  0.07494827,-0.02797735, -0.06948853,  0.06283715,  0.10689872,  0.02087112,0.05185082,  0.06266276,  0.01831927,  0.10564604,  0.00259254,0.08089193, -0.01426479,  0.00684974, -0.03707304, -0.1198062 ,-0.05715216,  0.01687549,  0.03455462, -0.08835565,  0.05120559,-0.06600516, -0.01664807, -0.02856736,  0.02654157, -0.00975818,-0.03065236, -0.04041981, -0.01071312, -0.05153402, -0.14723714,-0.00877744,  0.08035714,  0.00351824, -0.10722714, -0.03078206,-0.00496383, -0.01665388,  0.0004069 , -0.02276175,  0.14360192,-0.09488932,  0.00554548,  0.13301958, -0.02263096, -0.03730701,0.03650629, -0.02395339,  0.00687372, -0.02563804,  0.03732518,-0.02720424, -0.0106114 , -0.05050805,  0.00444685, -0.02968924,0.07124983, -0.00694057,  0.00107829, -0.08331589, -0.03359186,0.0081293 , -0.0008138 ,  0.01801554,  0.02518827, -0.03804089,0.06714594,  0.00194731,  0.08901033,  0.06102903,  0.03237479,-0.05186026,  0.02203078, -0.02689325, -0.01497105, -0.07096935,0.00406174,  0.03199695, -0.05650693, -0.00124395,  0.08180745,0.10938081,  0.0316787 ,  0.01944987, -0.02388909,  0.00355748,0.0249256 ,  0.00739524,  0.0506243 , -0.01226516,  0.01143035,-0.09211658, -0.02129836, -0.11622447, -0.04239509, -0.05391511,-0.00467064, -0.01021031,  0.00030227,  0.12456985, -0.0130964 ,0.02393832, -0.04647537,  0.06130255,  0.02752686,  0.04820469,-0.06352307,  0.0357637 , -0.1455921 ,  0.01995268, -0.04385739,-0.03136626, -0.04338237, -0.08235096,  0.02723331, -0.01401483])

fig = plt.figure(figsize=(16, 16))
plot_LSA(embeddings, list_labels)
plt.show()

这看起来就好多啦！

clf_w2v = LogisticRegression(C=30.0, class_weight='balanced', solver='newton-cg', multi_class='multinomial', random_state=40)
clf_w2v.fit(X_train_word2vec, y_train_word2vec)
y_predicted_word2vec = clf_w2v.predict(X_test_word2vec)

accuracy_word2vec, precision_word2vec, recall_word2vec, f1_word2vec = get_metrics(y_test_word2vec, y_predicted_word2vec)
print("accuracy = %.3f, precision = %.3f, recall = %.3f, f1 = %.3f" % (accuracy_word2vec, precision_word2vec, recall_word2vec, f1_word2vec))

accuracy = 0.777, precision = 0.776, recall = 0.777, f1 = 0.777

cm_w2v = confusion_matrix(y_test_word2vec, y_predicted_word2vec)
fig = plt.figure(figsize=(10, 10))
plot = plot_confusion_matrix(cm, classes=['Irrelevant','Disaster','Unsure'], normalize=False, title='Confusion matrix')
plt.show()
print("Word2Vec confusion matrix")
print(cm_w2v)
print("TFIDF confusion matrix")
print(cm2)
print("BoW confusion matrix")
print(cm)

Word2Vec confusion matrix
[[980 242   2][232 711   2][  2   5   0]]
TFIDF confusion matrix
[[974 249   1][261 684   0][  3   4   0]]
BoW confusion matrix
[[970 251   3][274 670   1][  3   4   0]]

这是目前为止最好的啦

基于深度学习的自然语言处理（CNN与RNN）

from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.utils import to_categoricalEMBEDDING_DIM = 300
MAX_SEQUENCE_LENGTH = 35
VOCAB_SIZE = len(VOCAB)VALIDATION_SPLIT=.2
tokenizer = Tokenizer(num_words=VOCAB_SIZE)
tokenizer.fit_on_texts(clean_questions["text"].tolist())
sequences = tokenizer.texts_to_sequences(clean_questions["text"].tolist())word_index = tokenizer.word_index
print('Found %s unique tokens.' % len(word_index))cnn_data = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)
labels = to_categorical(np.asarray(clean_questions["class_label"]))indices = np.arange(cnn_data.shape[0])
np.random.shuffle(indices)
cnn_data = cnn_data[indices]
labels = labels[indices]
num_validation_samples = int(VALIDATION_SPLIT * cnn_data.shape[0])embedding_weights = np.zeros((len(word_index)+1, EMBEDDING_DIM))
for word,index in word_index.items():embedding_weights[index,:] = word2vec[word] if word in word2vec else np.random.rand(EMBEDDING_DIM)
print(embedding_weights.shape)

Found 19098 unique tokens.
(19099, 300)

Now, we will define a simple Convolutional Neural Network

from keras.layers import Dense, Input, Flatten, Dropout, Merge
from keras.layers import Conv1D, MaxPooling1D, Embedding
from keras.layers import LSTM, Bidirectional
from keras.models import Modeldef ConvNet(embeddings, max_sequence_length, num_words, embedding_dim, labels_index, trainable=False, extra_conv=True):embedding_layer = Embedding(num_words,embedding_dim,weights=[embeddings],input_length=max_sequence_length,trainable=trainable)sequence_input = Input(shape=(max_sequence_length,), dtype='int32')embedded_sequences = embedding_layer(sequence_input)# Yoon Kim model (https://arxiv.org/abs/1408.5882)convs = []filter_sizes = [3,4,5]for filter_size in filter_sizes:l_conv = Conv1D(filters=128, kernel_size=filter_size, activation='relu')(embedded_sequences)l_pool = MaxPooling1D(pool_size=3)(l_conv)convs.append(l_pool)l_merge = Merge(mode='concat', concat_axis=1)(convs)# add a 1D convnet with global maxpooling, instead of Yoon Kim modelconv = Conv1D(filters=128, kernel_size=3, activation='relu')(embedded_sequences)pool = MaxPooling1D(pool_size=3)(conv)if extra_conv==True:x = Dropout(0.5)(l_merge)  else:# Original Yoon Kim modelx = Dropout(0.5)(pool)x = Flatten()(x)x = Dense(128, activation='relu')(x)#x = Dropout(0.5)(x)preds = Dense(labels_index, activation='softmax')(x)model = Model(sequence_input, preds)model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['acc'])return model

训练网络

x_train = cnn_data[:-num_validation_samples]
y_train = labels[:-num_validation_samples]
x_val = cnn_data[-num_validation_samples:]
y_val = labels[-num_validation_samples:]model = ConvNet(embedding_weights, MAX_SEQUENCE_LENGTH, len(word_index)+1, EMBEDDING_DIM, len(list(clean_questions["class_label"].unique())), False)

model.fit(x_train, y_train, validation_data=(x_val, y_val), epochs=3, batch_size=128)

Train on 8701 samples, validate on 2175 samples
Epoch 1/3
8701/8701 [==============================] - 11s - loss: 0.5964 - acc: 0.7067 - val_loss: 0.4970 - val_acc: 0.7848
Epoch 2/3
8701/8701 [==============================] - 11s - loss: 0.4434 - acc: 0.8019 - val_loss: 0.4722 - val_acc: 0.8005
Epoch 3/3
8701/8701 [==============================] - 11s - loss: 0.3968 - acc: 0.8283 - val_loss: 0.4985 - val_acc: 0.7880
<keras.callbacks.History at 0x12237bc88>

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