以某寶購物評論為例（表分為好評和差評）

好評

df = pd.read_excel("F:/文本大數據/購物評論.xlsx",sheet_name="正向",header=None) df

標記為1

df['class']=1 df.head()

差評

df1 = pd.read_excel("F:/文本大數據/購物評論.xlsx",sheet_name="負向",header=None) df1

標記為0

df1['class']=0 df1.head()

合并

df2 = df.append(df1,ignore_index=True) df2

分詞和預處理

import jieba cuttxt = lambda x:" ".join(jieba.lcut(x)) df2["segment"]=df2[0].apply(cuttxt) df2.head()

詞袋模型

from sklearn.feature_extraction.text import CountVectorizer # 詞袋模型 countvec = CountVectorizer() countvec = countvec.fit_transform(df2["segment"]) countvec

from sklearn.model_selection import train_test_split x_train,x_test,y_train,y_test = train_test_split(countvec,df2['class'],test_size=0) x_train

from sklearn.svm import SVC clf = SVC()# 支持向量機 clf.fit(x_train,y_train)

clf.score(x_train,y_train)

這里會花較長的時間，需要耐心等待！

如果想提高準確率可以通過改變參數：

邏輯回歸模型

from sklearn.linear_model import LogisticRegression logistic = LogisticRegression() logistic.fit(x_train,y_train)

logistic.score(x_train,y_train)

模型評估

SVM

from sklearn.metrics import classification_report print(classification_report(y_test,clf.predict(x_test))) s = """輸入你想預測的評論""" s_seg = " ".join(jieba.lcut(s)) s_seg_vec = countvec.transform([s_seg]) result = clf.predict(s_seg_vec) result

邏輯回歸

from sklearn.metrics import classification_report print(classification_report(y_test,logistic.predict(x_text))) s = """輸入你想預測的評論""" s_seg = " ".join(jieba.lcut(s)) s_seg_vec = countvec.transform([s_seg]) result = logistic.predict(s_seg_vec) result

總結

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