中文微博badword分類

import jieba
import numpy as np
import xml.dom.minidom
import random
from gensim.models import Word2Vec
from gensim.corpora.dictionary import Dictionary
from keras.preprocessing.sequence import pad_sequences
from keras.utils import to_categorical
from keras.layers import Dense, Input, Flatten,Activation,Dropout,Merge  
from keras.layers import Conv1D, MaxPooling1D, Embedding,GlobalMaxPooling1D
from keras.models import Model
from keras.models import Sequential
from gensim.models.keyedvectors import KeyedVectors
from keras.callbacks import EarlyStopping,ModelCheckpoint,Callback 
np.random.seed(1337)  

MAX_SEQUENCE_LENGTH = 70
MAX_NB_WORDS = 600000
EMBEDDING_DIM = 50
VALIDATION_SPLIT = 0.2
bwfile = open("badwordlist.txt",encoding='utf8')
bwlist = [line.strip('\n') for line in bwfile]
bwfile.close()
for bw in bwlist:
    jieba.add_word(bw)  
emo = ['like','fear','disgust','anger','surprise','sadness','happiness','none']
smallemo= ['angry/disgusted','happy/like','sad','afraid/surprised','other']
'''
stop_words = ["的", "一", "不", "在", "人", "有", "是", "為", "以", "于", "上", "他", "而",
            "后", "之", "來", "及", "了", "因", "下", "可", "到", "由", "這", "與", "也",
            "此", "但", "并", "個", "其", "已", "無", "小", "我", "們", "起", "最", "再",
            "今", "去", "好", "只", "又", "或", "很", "亦", "某", "把", "那", "你", "乃",
            "它","要", "將", "應(yīng)", "位", "新", "兩", "中", "更", "我們", "自己", "沒有", "“", "”",
            "针饥,", "(", ")", " ",'[',']',' ','~','祥得。','!',':','伤柄、','/','…']
'''
stop_words = []
sw = open('stopwords.txt')
for line in sw:
    line = line.strip("\n")
    stop_words.append(line)
sw.close()
def segmentWord(cont):
    c = []
    for i in cont:
        a = list(jieba.cut(i,cut_all=True))
        b = " ".join(a)
        c.append(b)
    return c

def text_to_index_array(p_new_dic, p_sen):  # 文本轉(zhuǎn)為索引數(shù)字模式
    new_sentences = []
    for sen in p_sen:
        new_sen = []
        for word in sen:
            try:
                new_sen.append(p_new_dic[word])  # 單詞轉(zhuǎn)索引數(shù)字
            except:
                new_sen.append(0)  # 索引字典里沒有的詞轉(zhuǎn)為數(shù)字0
        new_sentences.append(new_sen)

    return new_sentences

def readTrain(filename):
    DOMTree = xml.dom.minidom.parse(filename)
    collection = DOMTree.documentElement
    
    weibos = collection.getElementsByTagName("weibo")
    traindata = []
    trainlabel = []
    data = [[],[],[],[],[],[],[],[]]
    for weibo in weibos:
        sentence = weibo.getElementsByTagName('sentence')
        for e in sentence:
            sen = e.childNodes[0].data
            if e.getAttribute('opinionated')=='Y':
                emotion1 = e.getAttribute('emotion-1-type')
                emotion2 = e.getAttribute('emotion-2-type')
                data[emo.index(emotion1)].append(sen)
                if emotion2 != 'none':
                    data[emo.index(emotion2)].append(sen)        
            else:
                data[7].append(sen)
    #smalldata = [[],[],[],[],[]]
    smalldata = [[],[],[],[]]
    smalldata[0]=data[2]+data[3]
    smalldata[1]=data[0]+data[6]
    smalldata[2]=data[5]
    smalldata[3]=data[1]+data[4]
    smalldata[3] *= 2
    #smalldata[4]=data[7]
    for i,d in enumerate(smalldata):
        for ele in d:
            traindata.append(ele)
            trainlabel.append(i)
    return traindata,trainlabel


traindata,label = readTrain("Training data for Emotion Classification.xml")
#traindata,label = adjustData(traindata,label)
traindata = segmentWord(traindata)

testdata = []
tfile = open('withoutother.txt',encoding='utf8')
for line in tfile:
    testdata.append(line)
tfile.close()
testdata = segmentWord(testdata)
testlabel = []
labelfile = open('testlabelnoother.txt')
for l in labelfile:
    line = l.strip('\n')
    testlabel.append(smallemo.index(line))
labelfile.close()
traintexts = [[word for word in document.split() if word not in stop_words] for document in traindata]
testtexts = [[word for word in document.split() if word not in stop_words] for document in testdata]

word_vectors = KeyedVectors.load_word2vec_format('zhwiki_2017_03.sg_50d.word2vec', binary=False)
#word_vectors = Word2Vec(traintexts+testtexts, size=EMBEDDING_DIM, window=5, min_count=1)
#word_vectors.wv.save_word2vec_format('smallwv.txt',binary=False)
#word_vectors = KeyedVectors.load_word2vec_format('smallwv.txt', binary=False)

gensim_dict = Dictionary()
gensim_dict.doc2bow(word_vectors.vocab.keys(), allow_update=True)
w2indx = {v: k + 1 for k, v in gensim_dict.items()}  # 詞語的索引履澳,從1開始編號
w2vec = {word: word_vectors[word] for word in w2indx.keys()}
trainseq = text_to_index_array(w2indx, traintexts)
testseq = text_to_index_array(w2indx, testtexts)

traindata = pad_sequences(trainseq, maxlen=MAX_SEQUENCE_LENGTH)
testdata = pad_sequences(testseq, maxlen=MAX_SEQUENCE_LENGTH)
word_index = w2indx
print('Found %s unique tokens.' % len(word_index))
labels = to_categorical(np.asarray(label))
testlabels = to_categorical(np.asarray(testlabel))
indices = np.arange(traindata.shape[0])
np.random.shuffle(indices)
traindata = traindata[indices]
labels = labels[indices]


x_train = traindata[:] # 訓(xùn)練集
y_train = labels[:]# 訓(xùn)練集的標(biāo)簽
x_val =  testdata[:]# 測試集菩收,英文原意是驗證集
y_val =  testlabels[:]
print('Preparing embedding matrix.')
nb_words = min(MAX_NB_WORDS, len(word_index))
embedding_matrix = np.zeros((nb_words + 1, EMBEDDING_DIM))
for word, i in word_index.items():
    if i > MAX_NB_WORDS:
        continue
    embedding_vector = w2vec.get(word)
    if embedding_vector is not None:
        # words not found in embedding index will be all-zeros.
        embedding_matrix[i] = embedding_vector # word_index to word_embedding_vector ,<20000(nb_words)

# load pre-trained word embeddings into an Embedding layer
# note that we set trainable = False so as to keep the embeddings fixed
embedding_layer = Embedding(nb_words + 1,
                            EMBEDDING_DIM,
                            weights=[embedding_matrix],
                            input_length=MAX_SEQUENCE_LENGTH,
                            trainable=False)

print('Training model.')

model = Sequential()
model.add(embedding_layer)
model.add(Conv1D(256,4,padding='valid',activation='relu',strides=1))
model.add(GlobalMaxPooling1D())
#model.add(Dropout(0.2)) 
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.2)) 
model.add(Dense(16))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(4))
model.add(Activation('softmax'))
# 優(yōu)化器我這里用了adadelta,也可以使用其他方法
model.compile(loss='categorical_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

# =下面開始訓(xùn)練堪澎,nb_epoch是迭代次數(shù)炼吴,可以高一些,訓(xùn)練效果會更好扁眯,但是訓(xùn)練會變慢
#early_stopping =EarlyStopping(monitor='val_loss', patience=2) 
#checkpointer = ModelCheckpoint(filepath='weights.hdf5', verbose=1, save_best_only=True)
model.fit(x_train, y_train,batch_size=64,epochs=4,validation_data=(x_val,y_val))
pre = model.predict_classes(x_val,verbose=0,batch_size=64)

prelabel = []
predlabelfile = open("predfile.txt",'w+')
for p in pre:
    predlabelfile.write(str(smallemo[p])+'\n')
predlabelfile.close()
最后編輯于
?著作權(quán)歸作者所有,轉(zhuǎn)載或內(nèi)容合作請聯(lián)系作者
  • 序言:七十年代末壮莹,一起剝皮案震驚了整個濱河市,隨后出現(xiàn)的幾起案子恋拍,更是在濱河造成了極大的恐慌垛孔,老刑警劉巖,帶你破解...
    沈念sama閱讀 222,627評論 6 517
  • 序言:濱河連續(xù)發(fā)生了三起死亡事件施敢,死亡現(xiàn)場離奇詭異周荐,居然都是意外死亡狭莱,警方通過查閱死者的電腦和手機(jī),發(fā)現(xiàn)死者居然都...
    沈念sama閱讀 95,180評論 3 399
  • 文/潘曉璐 我一進(jìn)店門概作,熙熙樓的掌柜王于貴愁眉苦臉地迎上來腋妙,“玉大人,你說我怎么就攤上這事讯榕≈杷兀” “怎么了?”我有些...
    開封第一講書人閱讀 169,346評論 0 362
  • 文/不壞的土叔 我叫張陵愚屁,是天一觀的道長济竹。 經(jīng)常有香客問我,道長霎槐,這世上最難降的妖魔是什么送浊? 我笑而不...
    開封第一講書人閱讀 60,097評論 1 300
  • 正文 為了忘掉前任,我火速辦了婚禮丘跌,結(jié)果婚禮上袭景,老公的妹妹穿的比我還像新娘。我一直安慰自己闭树,他們只是感情好耸棒,可當(dāng)我...
    茶點故事閱讀 69,100評論 6 398
  • 文/花漫 我一把揭開白布。 她就那樣靜靜地躺著报辱,像睡著了一般与殃。 火紅的嫁衣襯著肌膚如雪。 梳的紋絲不亂的頭發(fā)上碍现,一...
    開封第一講書人閱讀 52,696評論 1 312
  • 那天奈籽,我揣著相機(jī)與錄音,去河邊找鬼鸵赫。 笑死,一個胖子當(dāng)著我的面吹牛躏升,可吹牛的內(nèi)容都是我干的辩棒。 我是一名探鬼主播,決...
    沈念sama閱讀 41,165評論 3 422
  • 文/蒼蘭香墨 我猛地睜開眼膨疏,長吁一口氣:“原來是場噩夢啊……” “哼一睁!你這毒婦竟也來了?” 一聲冷哼從身側(cè)響起佃却,我...
    開封第一講書人閱讀 40,108評論 0 277
  • 序言:老撾萬榮一對情侶失蹤者吁,失蹤者是張志新(化名)和其女友劉穎,沒想到半個月后饲帅,有當(dāng)?shù)厝嗽跇淞掷锇l(fā)現(xiàn)了一具尸體复凳,經(jīng)...
    沈念sama閱讀 46,646評論 1 319
  • 正文 獨居荒郊野嶺守林人離奇死亡,尸身上長有42處帶血的膿包…… 初始之章·張勛 以下內(nèi)容為張勛視角 年9月15日...
    茶點故事閱讀 38,709評論 3 342
  • 正文 我和宋清朗相戀三年,在試婚紗的時候發(fā)現(xiàn)自己被綠了炫彩。 大學(xué)時的朋友給我發(fā)了我未婚夫和他白月光在一起吃飯的照片改执。...
    茶點故事閱讀 40,861評論 1 353
  • 序言:一個原本活蹦亂跳的男人離奇死亡,死狀恐怖髓棋,靈堂內(nèi)的尸體忽然破棺而出实檀,到底是詐尸還是另有隱情,我是刑警寧澤按声,帶...
    沈念sama閱讀 36,527評論 5 351
  • 正文 年R本政府宣布膳犹,位于F島的核電站,受9級特大地震影響签则,放射性物質(zhì)發(fā)生泄漏须床。R本人自食惡果不足惜,卻給世界環(huán)境...
    茶點故事閱讀 42,196評論 3 336
  • 文/蒙蒙 一怀愧、第九天 我趴在偏房一處隱蔽的房頂上張望侨颈。 院中可真熱鬧,春花似錦芯义、人聲如沸哈垢。這莊子的主人今日做“春日...
    開封第一講書人閱讀 32,698評論 0 25
  • 文/蒼蘭香墨 我抬頭看了看天上的太陽耘分。三九已至,卻和暖如春绑警,著一層夾襖步出監(jiān)牢的瞬間求泰,已是汗流浹背。 一陣腳步聲響...
    開封第一講書人閱讀 33,804評論 1 274
  • 我被黑心中介騙來泰國打工计盒, 沒想到剛下飛機(jī)就差點兒被人妖公主榨干…… 1. 我叫王不留渴频,地道東北人。 一個月前我還...
    沈念sama閱讀 49,287評論 3 379
  • 正文 我出身青樓北启,卻偏偏與公主長得像卜朗,于是被迫代替她去往敵國和親。 傳聞我的和親對象是個殘疾皇子咕村,可洞房花燭夜當(dāng)晚...
    茶點故事閱讀 45,860評論 2 361

推薦閱讀更多精彩內(nèi)容