import?jieba
import?pandas?as?pd
#?加載情感詞典
posdict?=?pd.read_excel('positive_words.xlsx',?header=None)[0].tolist()
negdict?=?pd.read_excel('negative_words.xlsx',?header=None)[0].tolist()
#?分詞
text?=?'今天天氣真好，心情非常愉快。'
words?=?jieba.lcut(text)
#?計(jì)算情感得分
poscount?=?0
negcount?=?0
for?word?in?words:
????if?word?in?posdict:
????????poscount?+=?1
????elif?word?in?negdict:
????????negcount?+=?1
score?=?(poscount?-?negcount)?/?len(words)
print(score)

import?jieba
from?sklearn.feature_extraction.text?import?CountVectorizer
from?sklearn.naive_bayes?import?MultinomialNB
#?加載訓(xùn)練數(shù)據(jù)
posdata?=?pd.read_excel('positive_data.xlsx',?header=None)[0].tolist()
negdata?=?pd.read_excel('negative_data.xlsx',?header=None)[0].tolist()
data?=?posdata?+?negdata
labels?=?[1]?*?len(posdata)?+?[0]?*?len(negdata)
#?分詞
words?=?['?'.join(jieba.lcut(text))?for?text?in?data]
#?特征提取
vectorizer?=?CountVectorizer()
X?=?vectorizer.fit_transform(words)
#?訓(xùn)練分類器
clf?=?MultinomialNB()
clf.fit(X,?labels)
#?預(yù)測(cè)情感
text?=?'今天天氣真好，心情非常愉快。'
test_X?=?vectorizer.transform(['?'.join(jieba.lcut(text))])
score?=?clf.predict_proba(test_X)[0][1]
print(score)

import?jieba
from?keras.models?import?Sequential
from?keras.layers?import?Embedding,?Conv1D,?GlobalMaxPooling1D,?Dense
#?加載訓(xùn)練數(shù)據(jù)
posdata?=?pd.read_excel('positive_data.xlsx',?header=None)[0].tolist()
negdata?=?pd.read_excel('negative_data.xlsx',?header=None)[0].tolist()
data?=?posdata?+?negdata
labels?=?[1]?*?len(posdata)?+?[0]?*?len(negdata)
#?分詞
words?=?[jieba.lcut(text)?for?text?in?data]
#?構(gòu)建詞向量
word2vec?=?{}
with?open('sgns.weibo.bigram',?encoding='utf-8')?as?f:
????for?line?in?f:
????????line?=?line.strip().split()
????????word?=?line[0]
????????vec?=?[float(x)?for?x?in?line[1:]]
????????word2vec[word]?=?vec
embedding_matrix?=?[]
for?word?in?vectorizer.get_feature_names():
????if?word?in?word2vec:
????????embedding_matrix.append(word2vec[word])
????else:
????????embedding_matrix.append([0]?*?300)
#?構(gòu)建模型
model?=?Sequential()
model.add(Embedding(len(vectorizer.get_feature_names()),?300,?weights=[embedding_matrix],?input_length=100))
model.add(Conv1D(128,?5,?activation='relu'))
model.add(GlobalMaxPooling1D())
model.add(Dense(1,?activation='sigmoid'))
model.compile(optimizer='adam',?loss='binary_crossentropy',?metrics=['accuracy'])
#?訓(xùn)練模型
X?=?vectorizer.transform(['?'.join(words[i][:100])?for?i?in?range(len(words))]).toarray()
model.fit(X,?labels,?epochs=10,?batch_size=32)
#?預(yù)測(cè)情感
text?=?'今天天氣真好，心情非常愉快。'
test_X?=?vectorizer.transform(['?'.join(jieba.lcut(text)[:100])]).toarray()
score?=?model.predict(test_X)[0][0]
print(score)

import?jieba
import?pandas?as?pd
from?pyhanlp?import?*
#?加載情感知識(shí)圖譜
graph?=?pd.read_excel('emotion_graph.xlsx')
#?分詞
text?=?'今天天氣真好，心情非常愉快。'
words?=?jieba.lcut(text)
#?計(jì)算情感得分
poscount?=?0
negcount?=?0
for?word?in?words:
????if?word?in?graph['詞語'].tolist():
????????index?=?graph[graph['詞語']?==?word].index[0]
????????if?graph.loc[index,?'情感分類']?==?'正面':
????????????poscount?+=?1
????????elif?graph.loc[index,?'情感分類']?==?'負(fù)面':
????????????negcount?+=?1
score?=?(poscount?-?negcount)?/?len(words)
print(score)

import?jieba
import?pandas?as?pd
#?加載情感規(guī)則庫(kù)
rules?=?pd.read_excel('emotion_rules.xlsx')
#?分句
text?=?'今天天氣真好，心情非常愉快。'
sentences?=?HanLP.extractSummary(text,?3)
#?計(jì)算情感得分
poscount?=?0
negcount?=?0
for?sentence?in?sentences:
????for?index,?row?in?rules.iterrows():
????????if?row['情感詞']?in?sentence?and?row['情感分類']?==?'正面':
????????????poscount?+=?1
????????elif?row['情感詞']?in?sentence?and?row['情感分類']?==?'負(fù)面':
????????????negcount?+=?1
score?=?(poscount?-?negcount)?/?len(sentences)
print(score)

import?jieba
import?pandas?as?pd
import?numpy?as?np
from?keras.models?import?load_model
#?加載情感神經(jīng)網(wǎng)絡(luò)
model?=?load_model('emotion_network.h5')
#?加載情感詞典
posdict?=?pd.read_excel('positive_words.xlsx',?header=None)[0].tolist()
negdict?=?pd.read_excel('negative_words.xlsx',?header=None)[0].tolist()
#?分詞
text?=?'今天天氣真好，心情非常愉快。'
words?=?jieba.lcut(text)
#?構(gòu)建輸入向量
X?=?np.zeros((1,?len(words)))
for?i,?word?in?enumerate(words):
????if?word?in?posdict:
????????X[0,?i]?=?1
????elif?word?in?negdict:
????????X[0,?i]?=?-1
#?預(yù)測(cè)情感
score?=?model.predict(X)[0][0]
print(score)