分詞–list of list格式

（df2的來源請看上一篇博客https://blog.csdn.net/dongzixian/article/details/103474094）

import jieba df2['cut'] = df2[0].apply(jieba.lcut) df2.head()

from sklearn.model_selection import train_test_split x_train,x_test,y_train,y_test = train_test_split(df2['cut'],df2['class'],test_size=0.3) x_train

from gensim.models.word2vec import Word2Vec model = Word2Vec(size=400,min_count=5) model.build_vocab(x_train) model.build_vocab(x_train)

model.wv.most_similar("好",topn=100)

model.wv.most_similar("不好",topn=100)

model.wv["不好"]

生成句向量

print(df2.cut[0])#（第一條） print("第一條評論分詞后詞個數為：",len(df2.cut[0]))

將第一條評論轉成word2vec矩陣

pd.DataFrame([model.wv[i] for i in df2.cut[0] if i in model.wv])

將第一條評論轉成向量

vec1 = pd.DataFrame([model.wv[i] for i in df2.cut[0] if i in model.wv]).mean() print(vec1)

生成word2vec句向量

def sen2vec(words):return pd.DataFrame([model.wv[i] for i in df2.cut[0] if i in model.wv]).mean() train_vec = pd.DataFrame([sen2vec(s) for s in x_train]) train_vec

分類模型–支持向量機

from sklearn.svm import SVC clf2 = SVC() clf2.fit(train_vec,y_train)

clf2.score(train_vec,y_train)

SVM

from sklearn.metrics import classification_report print(classification_report(y_train,clf2.predict(train_vec)))

生成測試集word2vec句向量

def sen2vec(words):return pd.DataFrame([model.wv[i] for i in words if i in model.wv]).mean() test_vec = pd.DataFrame([sen2vec(s) for s in x_test]) test_vec clf2.score(test_vec,y_test)

from sklearn.metrics import classification_report print(classification_report(y_test,clf2.predict(test_vec)))

預測新評論

s = """...新評論...""" s_seg1 = jieba.lcut(s) s_word2vec = pd.DataFrame([model.wv[i] for i in s_seg1 if i in model.wv]) s_word2vec s_vec = s_word2vec.mean() s_vec import numpy as np s_vec = np.array(s_vec) s_vec = s_vec.reshape(1,-1) result_svm = clf2.predict(s_vec) result_sum

總結

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