• 第一篇 数据清洗与分析部分
• 第二篇 可视化部分,
• 第三篇 朴素贝叶斯文本分类

• 支持向量机分类
• 支持向量机 网格搜索
• 临近法
• 决策树
• 随机森林
• bagging方法

import pandas as pd
import numpy as np
import  matplotlib.pyplot as  plt
import time

df=pd.read_excel("all_data_meituan.xlsx")[["comment","star"]]
df.head()

df.shape

(17400, 2)

df['sentiment']=df['star'].apply(lambda x:1 if x>30 else 0)
df=df.drop_duplicates() ## 去掉重复的评论
df=df.dropna()

X=pd.concat([df[['comment']],df[['comment']],df[['comment']]])
y=pd.concat([df.sentiment,df.sentiment,df.sentiment])
X.columns=['comment']
X.reset_index
X.shape

(3138, 1)

import jieba
def chinese_word_cut(mytext):return " ".join(jieba.cut(mytext))
X['cut_comment']=X["comment"].apply(chinese_word_cut)
X['cut_comment'].head()

Building prefix dict from the default dictionary ...
Loading model from cache C:\Users\FRED-H~1\AppData\Local\Temp\jieba.cache
Loading model cost 0.651 seconds.
Prefix dict has been built succesfully.0    还行 吧 ， 建议 不要 排队 那个 烤鸭 和 羊肉串 ， 因为 烤肉 时间 本来 就 不够...
1    去过 好 几次 了   东西 还是 老 样子   没 增添 什么 新花样   环境 倒 是 ...
2    一个 字 ： 好 ！ ！ ！   # 羊肉串 #   # 五花肉 #   # 牛舌 #   ...
3    第一次 来 吃 ， 之前 看过 好多 推荐 说 这个 好吃 ， 真的 抱 了 好 大 希望 ...
4    羊肉串 真的 不太 好吃 ， 那种 说 膻 不 膻 说 臭 不 臭 的 味 。 烤鸭 还 行...
Name: cut_comment, dtype: object

from sklearn.model_selection import  train_test_split
X_train,X_test,y_train,y_test= train_test_split(X,y,random_state=42,test_size=0.25)

def get_custom_stopwords(stop_words_file):with open(stop_words_file,encoding="utf-8") as f:custom_stopwords_list=[i.strip() for i in f.readlines()]return custom_stopwords_list

stop_words_file = "stopwords.txt"
stopwords = get_custom_stopwords(stop_words_file)
stopwords[-10:]

['100', '01', '02', '03', '04', '05', '06', '07', '08', '09']

from sklearn.feature_extraction.text import  CountVectorizer
vect=CountVectorizer()
vect

CountVectorizer(analyzer='word', binary=False, decode_error='strict',dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',lowercase=True, max_df=1.0, max_features=None, min_df=1,ngram_range=(1, 1), preprocessor=None, stop_words=None,strip_accents=None, token_pattern='(?u)\\b\\w\\w+\\b',tokenizer=None, vocabulary=None)

vect.fit_transform(X_train["cut_comment"])

<2353x1965 sparse matrix of type '<class 'numpy.int64'>'with 20491 stored elements in Compressed Sparse Row format>

vect.fit_transform(X_train["cut_comment"]).toarray().shape

(2353, 1965)

# pd.DataFrame(vect.fit_transform(X_train["cut_comment"]).toarray(),columns=vect.get_feature_names()).iloc[:10,:22]
# print(vect.get_feature_names())
# #  数据维数1956，不算很大（未使用停用词）

vect = CountVectorizer(token_pattern=u'(?u)\\b[^\\d\\W]\\w+\\b',stop_words=frozenset(stopwords)) # 去除停用词
pd.DataFrame(vect.fit_transform(X_train['cut_comment']).toarray(), columns=vect.get_feature_names()).head()
# 1691 columns,去掉以数字为特征值的列，减少了三列编程1691
# max_df = 0.8 # 在超过这一比例的文档中出现的关键词（过于平凡），去除掉。
# min_df = 3 # 在低于这一数量的文档中出现的关键词（过于独特），去除掉。

from sklearn.pipeline import make_pipeline
from sklearn.svm import SVC
from sklearn import  metrics
svc_cl=SVC()
pipe=make_pipeline(vect,svc_cl)
pipe.fit(X_train.cut_comment, y_train)

Pipeline(memory=None,steps=[('countvectorizer', CountVectorizer(analyzer='word', binary=False, decode_error='strict',dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',lowercase=True, max_df=1.0, max_features=None, min_df=1,ngram_range=(1, 1), preprocessor=None,stop_words=...,max_iter=-1, probability=False, random_state=None, shrinking=True,tol=0.001, verbose=False))])

y_pred = pipe.predict(X_test.cut_comment)
metrics.accuracy_score(y_test,y_pred)

0.6318471337579618

metrics.confusion_matrix(y_test,y_pred)

array([[  0, 289],[  0, 496]], dtype=int64)

支持向量机分类

from sklearn.svm import SVC
svc_cl=SVC() # 实例化
pipe=make_pipeline(vect,svc_cl)
pipe.fit(X_train.cut_comment, y_train)
y_pred = pipe.predict(X_test.cut_comment)
metrics.accuracy_score(y_test,y_pred)

0.6318471337579618

支持向量机 网格搜索

from sklearn.model_selection import GridSearchCV
from sklearn.svm import SVC
from sklearn.pipeline import  Pipeline
# svc=SVC(random_state=1)
from sklearn.linear_model import SGDClassifier
from sklearn.feature_extraction.text import TfidfTransformer
tfidf=TfidfTransformer()
# ('tfidf',
#                       TfidfTransformer()),
#                      ('clf',
#                       SGDClassifier(max_iter=1000)),
# svc=SGDClassifier(max_iter=1000)
svc=SVC()
# pipe=make_pipeline(vect,SVC)
pipe_svc=Pipeline([("scl",vect),('tfidf',tfidf),("clf",svc)])
para_range=[0.0001,0.001,0.01,0.1,1.0,10,100,1000]
para_grid=[{'clf__C':para_range,'clf__kernel':['linear']},{'clf__gamma':para_range,'clf__kernel':['rbf']}
]

gs=GridSearchCV(estimator=pipe_svc,param_grid=para_grid,cv=10,n_jobs=-1)

gs.fit(X_train.cut_comment,y_train)

GridSearchCV(cv=10, error_score='raise',estimator=Pipeline(memory=None,steps=[('scl', CountVectorizer(analyzer='word', binary=False, decode_error='strict',dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',lowercase=True, max_df=1.0, max_features=None, min_df=1,ngram_range=(1, 1), preprocessor=None,stop_words=frozenset({'...,max_iter=-1, probability=False, random_state=None, shrinking=True,tol=0.001, verbose=False))]),fit_params=None, iid=True, n_jobs=-1,param_grid=[{'clf__C': [0.0001, 0.001, 0.01, 0.1, 1.0, 10, 100, 1000], 'clf__kernel': ['linear']}, {'clf__gamma': [0.0001, 0.001, 0.01, 0.1, 1.0, 10, 100, 1000], 'clf__kernel': ['rbf']}],pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',scoring=None, verbose=0)

gs.best_estimator_.fit(X_train.cut_comment,y_train)

Pipeline(memory=None,steps=[('scl', CountVectorizer(analyzer='word', binary=False, decode_error='strict',dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',lowercase=True, max_df=1.0, max_features=None, min_df=1,ngram_range=(1, 1), preprocessor=None,stop_words=frozenset({'...,max_iter=-1, probability=False, random_state=None, shrinking=True,tol=0.001, verbose=False))])

y_pred = gs.best_estimator_.predict(X_test.cut_comment)
metrics.accuracy_score(y_test,y_pred)

0.9503184713375796

临近法

from sklearn.neighbors import  KNeighborsClassifier
knn=KNeighborsClassifier(n_neighbors=5,p=2,metric='minkowski')
pipe=make_pipeline(vect,knn)
pipe.fit(X_train.cut_comment, y_train)

Pipeline(memory=None,steps=[('countvectorizer', CountVectorizer(analyzer='word', binary=False, decode_error='strict',dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',lowercase=True, max_df=1.0, max_features=None, min_df=1,ngram_range=(1, 1), preprocessor=None,stop_words=...owski',metric_params=None, n_jobs=1, n_neighbors=5, p=2,weights='uniform'))])

y_pred = pipe.predict(X_test.cut_comment)
metrics.accuracy_score(y_test,y_pred)

0.7070063694267515

metrics.confusion_matrix(y_test,y_pred)

array([[ 87, 202],[ 28, 468]], dtype=int64)

决策树

from sklearn.tree import DecisionTreeClassifier
tree=DecisionTreeClassifier(criterion='entropy',random_state=1)

pipe=make_pipeline(vect,tree)
pipe.fit(X_train.cut_comment, y_train)
y_pred = pipe.predict(X_test.cut_comment)
metrics.accuracy_score(y_test,y_pred)

0.9388535031847134

metrics.confusion_matrix(y_test,y_pred)

array([[256,  33],[ 15, 481]], dtype=int64)

随机森林

from sklearn.ensemble import RandomForestClassifier
forest=RandomForestClassifier(criterion='entropy',random_state=1,n_jobs=2)
pipe=make_pipeline(vect,forest)
pipe.fit(X_train.cut_comment, y_train)
y_pred = pipe.predict(X_test.cut_comment)
metrics.accuracy_score(y_test,y_pred)
# 加上tfidf反而准确率96.5降低至95.0，

0.9656050955414013

metrics.confusion_matrix(y_test,y_pred)

array([[265,  24],[  3, 493]], dtype=int64)

bagging方法

from sklearn.ensemble import BaggingClassifier
from sklearn.tree import DecisionTreeClassifier
tree=DecisionTreeClassifier(criterion='entropy',random_state=1)
bag=BaggingClassifier(base_estimator=tree,n_estimators=10,max_samples=1.0,max_features=1.0,bootstrap=True,bootstrap_features=False,n_jobs=1,random_state=1)
pipe=make_pipeline(vect,tfidf,bag)
pipe.fit(X_train.cut_comment, y_train)
y_pred = pipe.predict(X_test.cut_comment)
metrics.accuracy_score(y_test,y_pred)  #  没用转化td-idf 93.2%, 加上转化步骤，准确率提升到95.5

0.9554140127388535

metrics.confusion_matrix(y_test,y_pred)

array([[260,  29],[  6, 490]], dtype=int64)