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Python机器学习——分析评论情感

一、实验目标

1、将文件评论分为积极评论和消极评论两类，其中消极评论包括答非所问，省略回答与拒绝回答

（本文中我暂且规定积极评论为0，消极评论为1）

二、实验思路

1、用jieba库，去除停用词等操作处理原始数据，并挑出数据中评论为空的值（nan）可以直接将预测值变为1（消极评论）

2、根据1500条数据量划分出训练集和测试集，对模型进行测试，并选取正确率最高的方法

（我最终选取的是朴素贝叶斯方法）

3、针对30万条数据进行处理，查看算法的可行性

4、处理更大的300万条数据

三、实验预备知识

1、csv文件操作

（1）文件的创建与打开

with open(filepath,mode,encoding="utf8",newline="") as f:
    rows = csv.reader(f)    #rows为一个迭代器(大列表套着小列表)
    for row in rows:
        print(rows)

A.mode:文件打开模式

r:只读模式，文件不存在泽报错，默认模式(文件指针位于文件末尾)
r+:只读模式，文件不存在泽报错(文件指针位于文件开头)
w:写入模式，文件不存在则自动报错，每次打开会覆盖原文件内容,文件不关闭则可以进行多次写入（只会在打开文件时清空文件内容）
w+:写入模式，文件不存在则自动报错，每次打开会覆盖原文件内容，文件不关闭则可以进行多次写入（只会在打开文件时清空文件内容，指针位置在文件内容末尾）
a:追加模式，文件不存在则会自动创建，从末尾追加，不可读。
a+:追加且可读模式，刚打开时文件指针就在文件末尾。

B.utf-8和utf-8-sig

”utf-8“ 是以字节为编码单元,它的字节顺序在所有系统中都是一样的,没有字节序问题,因此它不需要BOM,所以当用"utf-8"编码方式读取带有BOM的文件时,它会把BOM当做是文件内容来处理, 也就会发生类似上边的错误.

“utf-8-sig"中sig全拼为 signature 也就是"带有签名的utf-8”, 因此"utf-8-sig"读取带有BOM的"utf-8文件时"会把BOM单独处理,与文本内容隔离开,也是我们期望的结果.

（2）csv文件的写入

headers=[]
datas=[]

with open(filepath,mode,encoding="utf8",newline="") as f:
    rows = csv.reader(f)
    rows.writerow(headers)
    rows.writerows(datas)

2、大文件的查看与修改

推荐一个软件EmEditor，excel最多打开104万行数据，更大的数据量会导致excel崩溃，对于查看或修改更大的csv文件，此时最好使用EmEditor

3、jieba,sklearn等库的使用

代码里会详细解释

四、实验代码

import jieba
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.linear_model import LogisticRegression
from pandas import DataFrame
import openpyxl as op

#训练集
new_reply = pd.DataFrame(None,columns=["reply","answer"]) 
reply = pd.read_excel("sum.xlsx").astype(str)

print("训练集读取成功！")
#新数据
reply1 = pd.read_csv("result297.csv").astype(str)
new_reply1 = pd.DataFrame(None,columns=["reply","answer"])
print("数据集读取成功")
#stopwords的引用

with open("stopwords.txt","r",encoding = "utf-8") as f:
    stops = f.readlines()
stopwords = [x.strip() for x in stops]

#利用jieba对文本进行分段

new_reply["reply"] = reply.回答.apply(lambda s:" ".join(jieba.cut(s)))   
new_reply["answer"] = reply["answer（人工阅读）"]
new_reply["reply"] = new_reply.reply.apply(lambda s:s if str(s)!="nan" else np.nan)
df = new_reply["reply"]
df=df.to_frame(name="reply")
df_isnull_remark = df[df["reply"].isnull()]
new_reply.dropna(subset=["reply"],inplace=True)
cut_list = new_reply["reply"].apply(lambda s:s.split(" "))
result = cut_list.apply(lambda s:[x for x in s if x not in stopwords])   #result类型为series

new_reply1["reply"] = reply1.回答.apply(lambda s:" ".join(jieba.cut(s)))

# new_reply1["answer"] = reply1["answer（人工）"]
new_reply1["reply"] = new_reply1.reply.apply(lambda s:s if str(s)!="nan" else np.nan)
cf = new_reply1["reply"]
cf=cf.to_frame(name="reply")
cf_isnull_remark = cf[cf["reply"].isnull()]
new_reply1.dropna(subset=["reply"],inplace=True)
cut_list1 = new_reply1["reply"].apply(lambda s:s.split(" "))
result1 = cut_list1.apply(lambda s:[x for x in s if x not in stopwords])   #result1类型为series

# x_train,x_test,y_train,y_test = train_test_split(result,new_reply["answer"],test_size = 0.25,random_state = 3)

x_train = result
y_train = new_reply["answer"]

# test_index = x_test.index
test_index = result1.index

x_train = x_train.reset_index(drop=True)
# print(type(x_test))       #x_test类型为series

words=[]
for i in range(len(x_train)):
    words.append(' '.join(x_train[i]))
    
result1 = result1.reset_index(drop=True)

test_words=[]

for i in range(len(result1)):
    test_words.append(' '.join(result1[i]))

# print(test_words)

# vec = CountVectorizer(analyzer='word',max_features=4000,lowercase=False)
# vec.fit(words)
# classifer = MultinomialNB()
# classifer.fit(vec.transform(words),y_train)
# score = classifer.score(vec.transform(test_words),y_test)
# # a=classifer.predict_proba(vec.transform(test_words))
# print(score)

cv = CountVectorizer(ngram_range=(1,2))
cv.fit(words)
classifer = MultinomialNB()
classifer.fit(cv.transform(words),y_train)
# score = classifer.score(cv.transform(test_words),test)
a=classifer.predict_proba(cv.transform(test_words))
# print(score)

# tv = TfidfVectorizer(analyzer='word',max_features = 4000,ngram_range=(1,2),lowercase=False)
# tv.fit(words)
# classifer = MultinomialNB()
# classifer.fit(tv.transform(words),y_train)
# score = classifer.score(tv.transform(test_words),y_test)
# a=classifer.predict_proba(tv.transform(test_words))
# print(score)

# b=[]
# c=[]
# for nums in a:
    
#     for num in nums:
#         num = int(num +0.5)
#         b.append(num)
#     if(b[0] == 1):
#         c.append(1)
#     if(b[1] == 1):
#         c.append(0)
#     b=[]
        

# print(cf_isnull_remark.index)
# tableAll = op.load_workbook('201401.xlsx')
# table1 = tableAll['Sheet1']

# for i in range(len(c)):
#     table1.cell(test_index[i]+2, 12, c[i])8i
# for i in range(len(df_isnull_remark.index)):
#     table1.cell(df_isnull_remark.index[i]+2, 12, 0)
    
# tableAll.save('201401.xlsx')

# judge=Ture
# p=1
# with open("result297.csv","r",encoding='utf-8',newline='') as f:
#     rows = csv.reader(f)
#     with open("help297.csv", 'w',encoding='utf-8',newline='') as file:
#         writer = csv.writer(file)
#         for row in rows:
#             if(p==1):
#                 row.append("result")
#                 p+=1
#             else:
#                 for o in range(len(c)):
#                     if(p==test_index[o]+2):
#                         row.append(c[o])
#                         break
#                 for u in range(len(cf_isnull_remark.index)):
                    
#                     if(p==cf_isnull_remark.index[u]+2):
#                         row.append(1)
#                         break
#                 p+=1
#             writer.writerow(row)

print("ok")

五、部分代码解析

1、jieba分词部分

#利用jieba对文本进行分段

new_reply["reply"] = reply.回答.apply(lambda s:" ".join(jieba.cut(s)))   
new_reply["answer"] = reply["answer（人工阅读）"]
cut_list = new_reply["reply"].apply(lambda s:s.split(" "))
#new_reply["reply"]为DataFrame类型，通过使用""切分将cut_list转化为Series类型

jieba分词的作用如下所示，它会将输入的参数自动划分为诸多词块

8e13cb3bfc5649318749f1c87c61dea9.png

2、去除停用词

停用词为评论中一些对于结果的分类影响不大的词语，一般在网上都可以查到。

删除的方法为遍历整个列表，找到跟停用词相同的元素立即删除并继续循环，直到去掉评论中的所有停用词

aa30d6305ea44add9024747998fabb05.png

with open("stopwords.txt","r",encoding = "utf-8") as f:
    stops = f.readlines()
stopwords = [x.strip() for x in stops]

#strip()删除无意义换行符，空格等等

cut_list1 = new_reply1["reply"].apply(lambda s:s.split(" "))
result1 = cut_list1.apply(lambda s:[x for x in s if x not in stopwords])

3、划分训练集和测试集

x_train,x_test,y_train,y_test = train_test_split(result,new_reply["answer"],test_size = 0.25,random_state = 3)

#此处选取75%数据作训练集，25%数据作测试集

4、利用机器学习方法进行训练

# vec = CountVectorizer(analyzer='word',max_features=4000,lowercase=False)
# vec.fit(words)
# classifer = MultinomialNB()
# classifer.fit(vec.transform(words),y_train)
# score = classifer.score(vec.transform(test_words),y_test)
# # a=classifer.predict_proba(vec.transform(test_words))
# print(score)

cv = CountVectorizer(ngram_range=(1,2))
cv.fit(words)
classifer = MultinomialNB()
classifer.fit(cv.transform(words),y_train)
# score = classifer.score(cv.transform(test_words),test)
a=classifer.predict_proba(cv.transform(test_words))
# print(score)

# tv = TfidfVectorizer(analyzer='word',max_features = 4000,ngram_range=(1,2),lowercase=False)
# tv.fit(words)
# classifer = MultinomialNB()
# classifer.fit(tv.transform(words),y_train)
# score = classifer.score(tv.transform(test_words),y_test)
# a=classifer.predict_proba(tv.transform(test_words))
# print(score)

cv.fit函数将训练集划分的词块投喂进去，统计有效词词频。

classifer.predict_proba函数可以统计差异，计算正确率，经计算，利用朴素贝叶斯分类的正确率可以达到91%，效果较为理想。

42fd572585364f5bb3afa9925124e98d.png

5、大csv文件的处理操作具体实现

处理三百万条数据时python读入导致计算机卡顿，于是我将数据进行拆分成10000条数据的小csv文件并对小文件进行操作，最后将得到结果的所有文件再进行合并。

A、拆分为300个小文件=========>>

import csv
import os

#准备10000行的csv作为样例，进行拆分
example_path = 'data1.csv' # 需要拆分文件的路径
example_result_dir = 'chaifen' # 拆分后文件的路径

with open(example_path, 'r', newline='',encoding = 'utf-8') as example_file:
    example = csv.reader(example_file)
    i = j = 1
    for row in example:
#         print(row)
#         print(f'i 等于 {i}, j 等于 {j}')
        # 每1000个就j加1， 然后就有一个新的文件名
        if i % 10000 == 0:
            
            print(f'第{j}个文件生成完成')
            j += 1
            
        example_result_path = example_result_dir + '\\result' + str(j) + '.csv'
#         print(example_result_path)
        
        # 不存在此文件的时候，就创建
        if not os.path.exists(example_result_path):
            with open(example_result_path, 'w', newline='',encoding = 'utf-8-sig') as file:
                csvwriter = csv.writer(file)
                csvwriter.writerow(['code', '提问', '回答', '回答时间', 'Question', 'NegAnswer_Reg', '查找方式', 'year','DueOccup', 'Gender', 'SOEearly', 'Exchange', 'NegAnswer_Lasso','Length', 'Salary', 'TotleQuestion', 'Analyst', 'NegTone', 'AccTerm','Readability', 'ShareHold', 'InstOwn', 'SOE', '行业名称', '行业代码','industry', 'quarter'])
                csvwriter.writerow(row)
            i += 1
            
            
        # 存在的时候就往里面添加
        else:
            with open(example_result_path, 'a', newline='',encoding = 'utf-8') as file:
                csvwriter = csv.writer(file)
                csvwriter.writerow(row)
            i += 1

6bc8b0ff5a7c4e918c3a9c6eb8bc2f7f.png

B、分别进行处理=========>>

import jieba
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.linear_model import LogisticRegression
from pandas import DataFrame
import openpyxl as op
import threading
from concurrent.futures import ThreadPoolExecutor
import time
import csv

#训练集
new_reply = pd.DataFrame(None,columns=["reply","answer"]) 
reply = pd.read_excel("sum.xlsx").astype(str)

print("训练集读取成功！")
# print(reply.head())

with open("stopwords.txt","r",encoding = "utf-8") as f:
    stops = f.readlines()
stopwords = [x.strip() for x in stops]

#利用jieba对文本进行分段

new_reply["reply"] = reply.回答.apply(lambda s:" ".join(jieba.cut(s)))   
new_reply["answer"] = reply["answer（人工阅读）"]
new_reply["reply"] = new_reply.reply.apply(lambda s:s if str(s)!="nan" else np.nan)
df = new_reply["reply"]
df=df.to_frame(name="reply")
df_isnull_remark = df[df["reply"].isnull()]
new_reply.dropna(subset=["reply"],inplace=True)
cut_list = new_reply["reply"].apply(lambda s:s.split(" "))
result = cut_list.apply(lambda s:[x for x in s if x not in stopwords])   #result类型为series
x_train = result
y_train = new_reply["answer"]

# print(new_reply.head())

time1 = time.time()

x_train = x_train.reset_index(drop=True)
words=[]
for i in range(len(x_train)):
    words.append(' '.join(x_train[i]))

    
cv = CountVectorizer(ngram_range=(1,2))
cv.fit(words)
classifer = MultinomialNB()
classifer.fit(cv.transform(words),y_train)
i=0
# print(words)    

#新数据
for i in range(296):
    t = time.time()
    path = './chaifen/result' + str(i+1) +'.csv'
    reply1 = pd.read_csv(path).astype(str)
    new_reply1 = pd.DataFrame(None,columns=["reply","answer"])
    print(f"第{i+1}个数据集读取成功")
    #stopwords的引用

    new_reply1["reply"] = reply1.回答.apply(lambda s:" ".join(jieba.cut(s)))
    new_reply1["reply"] = new_reply1.reply.apply(lambda s:s if str(s)!="nan" else np.nan)
    cf = new_reply1["reply"]
    cf=cf.to_frame(name="reply")
    cf_isnull_remark = cf[cf["reply"].isnull()]
    new_reply1.dropna(subset=["reply"],inplace=True)
    cut_list1 = new_reply1["reply"].apply(lambda s:s.split(" "))
    result1 = cut_list1.apply(lambda s:[x for x in s if x not in stopwords])   #result1类型为series
    test_index = result1.index

    # print(result1)

    result1 = result1.reset_index(drop=True)

    test_words=[]
    for j in range(len(result1)):
        test_words.append(' '.join(result1[j]))
    #     print(test_words[0])

    a=classifer.predict_proba(cv.transform(test_words))
    # print(ad)
    b=[]
    c=[]
    for nums in a:
        for num in nums:
            num = int(num +0.5)
            b.append(num)
        if(b[0] == 1):
            c.append(1)
        if(b[1] == 1):
            c.append(0)
        b=[]
    print(len(c))
    p=1
    with open(path,"r",encoding='utf-8',newline='') as f:
        rows = csv.reader(f)
        with open(f"./chaifen/help{i+1}.csv", 'w',encoding='utf-8',newline='') as file:
            writer = csv.writer(file)
            for row in rows:
                if(p==1):
                    row.append("result")
                    
                else:
                    for o in range(len(c)):
                        if(p==test_index[o]+2):
                            row.append(c[o])
                            break
                            
                    for u in range(len(cf_isnull_remark.index)):
                        if(p==cf_isnull_remark.index[u]+2):
                            row.append(1)
                            break
                
                writer.writerow(row)
                p+=1

    print(f"第{i+1}个数据集处理成功,用时{time.time() - t}")

print(f"总计用时{time.time() - time1}")
print("all is over!")

dba707f436bf4a05b076f57cb9a82edf.png

花了我一晚上时间，终于跑出了最终结果

（或许通过多线程可以加快速度，接下来再学习）

C、合并 =========>>

import time

for i in range(297):
    time1 = time.time()
    
    with open (f"./chaifen/help{i+1}.csv","r",newline='',encoding = "utf8") as af:
        rows = csv.reader(af)
        with open("./chaifen/a.csv","a+",encoding = "utf8",newline='') as bf:
            for row in rows:
                writer = csv.writer(bf)
                writer.writerow(row)

    print(f"第{i}个数据集已加入,用时{time.time()-time1}")
print(f"all is over,用时{time.time()-time1}")

完结撒花！

标签： python 开发语言 大数据
本文转载自: https://blog.csdn.net/Josepyth/article/details/126214007
版权归原作者 Josepyth 所有， 如有侵权，请联系我们删除。
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