情感分析(支持向量机，逻辑回归)

以某宝购物评论为例（表分为好评和差评）

好评

df = pd.read_excel("F:/文本大数据/购物评论.xlsx",sheet_name="正向",header=None)
df

标记为1

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

差评

df1 = pd.read_excel("F:/文本大数据/购物评论.xlsx",sheet_name="负向",header=None)
df1

标记为0

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

合并

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

分词和预处理

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

词袋模型

from sklearn.feature_extraction.text import CountVectorizer

# 词袋模型
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()# 支持向量机
clf.fit(x_train,y_train)

clf.score(x_train,y_train)

这里会花较长的时间，需要耐心等待！

如果想提高准确率可以通过改变参数：

逻辑回归模型

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

logistic.score(x_train,y_train)

模型评估

SVM

from sklearn.metrics import classification_report
print(classification_report(y_test,clf.predict(x_test)))

s = """输入你想预测的评论"""
s_seg = " ".join(jieba.lcut(s))
s_seg_vec = countvec.transform([s_seg])
result = clf.predict(s_seg_vec)
result

逻辑回归

from sklearn.metrics import classification_report
print(classification_report(y_test,logistic.predict(x_text)))

s = """输入你想预测的评论"""
s_seg = " ".join(jieba.lcut(s))
s_seg_vec = countvec.transform([s_seg])
result = logistic.predict(s_seg_vec)
result