【NLP】文字情感分析

import pickle

import numpy as np

import pandas as pd

from keras.utils import np_utils

from keras.utils.vis_utils import plot_model

from keras.models import Sequential

from keras.preprocessing.sequence import pad_sequences

from keras.layers import LSTM, Dense, Embedding,Dropout

from sklearn.model_selection import train_test_split

from sklearn.metrics import accuracy_score

# load dataset

# ['evaluation'] is feature, ['label'] is label

def load_data(filepath,input_shape=20):

     df=pd.read_csv(filepath)

     # 標籤及詞彙表

     labels,vocabulary=list(df['label'].unique()),list(df['evaluation'].unique())

     # 構造字元級別的特徵

     string=''

     for word in vocabulary:

         string+=word

     vocabulary=set(string)

     # 字典列表

     word_dictionary={word:i+1 for i,word in enumerate(vocabulary)}

     with open('word_dict.pk','wb') as f:

         pickle.dump(word_dictionary,f)

     inverse_word_dictionary={i+1:word for i,word in enumerate(vocabulary)}

     label_dictionary={label:i for i,label in enumerate(labels)}

     with open('label_dict.pk','wb') as f:

         pickle.dump(label_dictionary,f)

     output_dictionary={i:labels for i,labels in enumerate(labels)}

     # 詞彙表大小

     vocab_size=len(word_dictionary.keys())

     # 標籤類別數量

     label_size=len(label_dictionary.keys())

     # 序列填充，按 input_shape 填充，長度不足的按 0 補充

     x=[[word_dictionary[word] for word in sent] for sent in df['evaluation']]

     x=pad_sequences(maxlen=input_shape,sequences=x,padding='post',value=0)

     y=[[label_dictionary[sent]] for sent in df['label']]

     '''

     np_utils.to_categorical 用於將標籤轉化為形如 (nb_samples, nb_classes)

     的二值序列。

     假設 num_classes = 10 。

     如將 [1, 2, 3, …… 4] 轉化成：

     [[0, 1, 0, 0, 0, 0, 0, 0]

      [0, 0, 1, 0, 0, 0, 0, 0]

      [0, 0, 0, 1, 0, 0, 0, 0]

     ……

     [0, 0, 0, 0, 1, 0, 0, 0]]

     '''

     y=[np_utils.to_categorical(label,num_classes=label_size) for label in y]

     y=np.array([list(_[0]) for _ in y])

     return x,y,output_dictionary,vocab_size,label_size,inverse_word_dictionary

# 建立深度學習模型， Embedding + LSTM + Softmax

def create_LSTM(n_units,input_shape,output_dim,filepath):

     x,y,output_dictionary,vocab_size,label_size,inverse_word_dictionary=load_data(filepath)

     model=Sequential()

     model.add(Embedding(input_dim=vocab_size+1,output_dim=output_dim,

                         input_length=input_shape,mask_zero=True))

     model.add(LSTM(n_units,input_shape=(x.shape[0],x.shape[1])))

     model.add(Dropout(0.2))

     model.add(Dense(label_size,activation='softmax'))

     model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])

     '''

         error:ImportError: ('You must install pydot (`pip install pydot`) and install graphviz (see instructions at ) ', 'for plot_model/model_to_dot to work.')

         版本問題： from keras.utils.vis_utils import plot_model

         真正解決方案：

     '''

     plot_model(model,to_file='./model_lstm.png',show_shapes=True)

     # 輸出模型資訊

     model.summary()

     return model

# 模型訓練

def model_train(input_shape,filepath,model_save_path):

     # 將資料集分為訓練集和測試集，佔比為 9 ： 1

     # input_shape=100

x,y,output_dictionary,vocab_size,label_size,inverse_word_dictionary=load_data(filepath,input_shape)

     train_x,test_x,train_y,test_y=train_test_split(x,y,test_size=0.1,random_state=42)

     # 模型輸入引數，需要根據自己需要調整

     n_units=100

     batch_size=32

     epochs=5

     output_dim=20

     # 模型訓練

     lstm_model=create_LSTM(n_units,input_shape,output_dim,filepath)

     lstm_model.fit(train_x,train_y,epochs=epochs,batch_size=batch_size,verbose=1)

     # 模型儲存

     lstm_model.save(model_save_path)

     # 測試條數

     N= test_x.shape[0]

     predict=[]

     label=[]

     for start,end in zip(range(0,N,1),range(1,N+1,1)):

         print(f'start:{start}, end:{end}')

         sentence=[inverse_word_dictionary[i] for i in test_x[start] if i!=0]

         y_predict=lstm_外匯跟單gendan5.commodel.predict(test_x[start:end])

         print('y_predict:',y_predict)

         label_predict=output_dictionary[np.argmax(y_predict[0])]

         label_true=output_dictionary[np.argmax(test_y[start:end])]

         print(f'label_predict:{label_predict}, label_true:{label_true}')

         # 輸出預測結果

         print(''.join(sentence),label_true,label_predict)

         predict.append(label_predict)

         label.append(label_true)

     # 預測準確率

     acc=accuracy_score(predict,label)

     print(' 模型在測試集上的準確率 :%s'%acc)

if __name__=='__main__':

     filepath='data/data_single.csv'

     input_shape=180

     model_save_path='data/corpus_model.h5'

     model_train(input_shape,filepath,model_save_path)