[Python人工智能] 三十五.基于Transformer的商品评论情感分析机器学习和深度学习的Baseline模型实现

Posted 2022-05-11 Eastmount

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篇首语：本文由小常识网(cha138.com)小编为大家整理，主要介绍了[Python人工智能] 三十五.基于Transformer的商品评论情感分析机器学习和深度学习的Baseline模型实现相关的知识，希望对你有一定的参考价值。

从本专栏开始，作者正式研究Python深度学习、神经网络及人工智能相关知识。前一篇文章通过keras-bert库构建Bert模型，并实现微博情感分析。这篇文章将利用Keras构建Transformer或多头自注意机制模型，并实现商品论文的情感分析，在这之前，我们先构建机器学习和深度学习的Baseline模型，只有不断强化各种模型的实现，才能让我们更加熟练地应用于自身研究领域和改进。基础性文章，希望对您有所帮助！

本专栏主要结合作者之前的博客、AI经验和相关视频及论文介绍，后面随着深入会讲解更多的Python人工智能案例及应用。基础性文章，希望对您有所帮助，如果文章中存在错误或不足之处，还请海涵！作者作为人工智能的菜鸟，希望大家能与我在这一笔一划的博客中成长起来。写了这么多年博客，尝试第一个付费专栏，为小宝赚点奶粉钱，但更多博客尤其基础性文章，还是会继续免费分享，该专栏也会用心撰写，望对得起读者。如果有问题随时私聊我，只望您能从这个系列中学到知识，一起加油喔~

Keras下载地址：https://github.com/eastmountyxz/AI-for-Keras
TensorFlow下载地址：https://github.com/eastmountyxz/AI-for-TensorFlow

前文赏析：

一.数据预处理

首先进行数据预处理，并给出整个项目的基本目录。

C:.
│  01-cutword.py
│  02-wordcloud.py
│  03-ml-model.py
│  04-CNN-model.py
│  05-TextCNN-model.py
│  06-BiLSTM-model.py
│  07-BiGRU-model.py
└─data
        data-word-count-train-happy.csv
        data-word-count-train-sad.csv
        online_shopping_10_cats.csv
        online_shopping_10_cats_words.csv
        online_shopping_10_cats_words_test.csv
        online_shopping_10_cats_words_train.csv

1.数据集

整个数据集来源于github，是一个包含十类商品的评论数据集，我们将对其进行情感分析研究。

整个数据集包含3个字段，分别是：

cat：商品类型
label：商品类别
review：评论内容

数据集存储在CSV文件中，如下图所示：

数据分布如下图所示：

2.中文分词

中文分词利用Jieba工具实现，处理后的结果如下图所示：

程序的运行结果如下图所示：

该部分代码如下所示：

# -*- coding: utf-8 -*-
"""
@author: xiuzhang Eastmount 2022-05-04
"""
import pandas as pd
import jieba
import csv
from collections import Counter

#-----------------------------------------------------------------------------
#样本数量统计
pd_all = pd.read_csv('data/online_shopping_10_cats.csv')
moods = 0: '消极', 1: '积极'

print('数目（总体）：%d' % pd_all.shape[0])
for label, mood in moods.items(): 
    print('数目（）：'.format(mood, pd_all[pd_all.label==label].shape[0]))


#-----------------------------------------------------------------------------
#中文分词和停用词过滤
cut_words = ""
all_words = ""
stopwords = ["[", "]", "）", "（", ")", "(", "【", "】", "！", "，", "$",
             "·", "？", ".", "、", "-", "—", ":", "：", "《", "》", "=",
             "。", "…", "“", "?", "”", "~", " ", "－", "+", "\\\\", "‘",
             "～", "；", "’", "...", "..", "&", "#",  "....", ",", 
             "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10"
             "的", "和", "之", "了", "哦", "那", "一个",  ]

fw = open('data/online_shopping_10_cats_words.csv', 'w', encoding='utf-8')
for line in range(len(pd_all)): #cat label review
    dict_cat = pd_all['cat'][line]
    dict_label = pd_all['label'][line]
    dict_review = pd_all['review'][line]
    dict_review = str(dict_review)
    #print(dict_cat, dict_label, dict_review)

    #jieba分词并过滤停用词
    cut_words = ""
    data = dict_review.strip('\\n')
    data = data.replace(",", "")    #一定要过滤符号 ","否则多列
    seg_list = jieba.cut(data, cut_all=False)
    for seg in seg_list:
        if seg not in stopwords:
            cut_words += seg + " "
    all_words += cut_words
    #print(cut_words)
    
    fw.write(str(dict_cat)+","+str(dict_label)+","+str(cut_words)+"\\n")
else:
    fw.close()

#-----------------------------------------------------------------------------
#词频统计
all_words = all_words.split()
print(all_words)

c = Counter()
for x in all_words:
    if len(x)>1 and x != '\\r\\n':
        c[x] += 1
        
#输出词频最高的前10个词
print('\\n词频统计结果：')
for (k,v) in c.most_common(10):
    print("%s:%d"%(k,v))

二.词云可视化分析

接着我们给“online_shopping_10_cats_words.csv”增加表头：cat、label和review，然后进行词云可视化分析。

第一步，安装Pyecharts扩展包。

pip install pyecharts

第二步，分别统计积极和消极情绪特征词的数量，并利用Counter函数统计高频词。

第三步，调用Pyecharts绘制词云。

积极和消极情感的最终词云如下图所示：

完整代码如下：

# -*- coding: utf-8 -*-
"""
@author: xiuzhang Eastmount 2022-05-04
"""
import pandas as pd
import csv
from collections import Counter

#-----------------------------------------------------------------------------
#读取分词后特征词
cut_words = ""
all_words = ""
pd_all = pd.read_csv('data/online_shopping_10_cats_words.csv')
moods = 0: '积极', 1: '消极'
print('数目（总体）：%d' % pd_all.shape[0])    
for line in range(len(pd_all)): #cat label review
    dict_cat = pd_all['cat'][line]
    dict_label = pd_all['label'][line]
    dict_review = pd_all['review'][line]
    if str(dict_label)=="1":  #积极
        cut_words = dict_review
        all_words += str(cut_words)
        #print(cut_words)

#-----------------------------------------------------------------------------
#词频统计
all_words = all_words.split()
c = Counter()
for x in all_words:
    if len(x)>1 and x != '\\r\\n':
        c[x] += 1
print('\\n词频统计结果：')
for (k,v) in c.most_common(10):
    print("%s:%d"%(k,v))
    
#存储数据
name ="data/data-word-count-train-happy.csv"
fw = open(name, 'w', encoding='utf-8')
i = 1
for (k,v) in c.most_common(len(c)):
    fw.write(str(i)+','+str(k)+','+str(v)+'\\n')
    i = i + 1
else:
    print("Over write file!")
    fw.close()

#-----------------------------------------------------------------------------
#词云分析
from pyecharts import options as opts
from pyecharts.charts import WordCloud
from pyecharts.globals import SymbolType

#生成数据 word = [('A',10), ('B',9), ('C',8)] 列表+Tuple
words = []
for (k,v) in c.most_common(120):
    words.append((k,v))

#渲染图
def wordcloud_base() -> WordCloud:
    c = (
        WordCloud()
        .add("", words, word_size_range=[20, 40], shape='diamond') #shape=SymbolType.ROUND_RECT
        .set_global_opts(title_opts=opts.TitleOpts(title='词云图'))
    )
    return c
wordcloud_base().render('电商评论积极情感分析词云图.html')

三.机器学习情感分析

为了更准确地评估模型，本文将分词后的数据集分成了训练集和测试集，并在相同环境进行对比实验。

具体流程如下：

数据读取及预处理
TF-IDF计算
分类模型构建
实验评估

输出结果如下图所示：

完整代码如下所示：

# -*- coding: utf-8 -*-
"""
@author: xiuzhang Eastmount 2022-05-04
"""
import jieba
import pandas as pd
import numpy as np
from collections import Counter
from scipy.sparse import coo_matrix
from sklearn import feature_extraction  
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn import svm
from sklearn import neighbors
from sklearn.naive_bayes import MultinomialNB
from sklearn.ensemble import AdaBoostClassifier

#-----------------------------------------------------------------------------
#变量定义
train_cat = []
test_cat = []
train_label = []
test_label = []
train_review = []
test_review = []

#读取数据
train_path = 'data/online_shopping_10_cats_words_train.csv'
test_path = 'data/online_shopping_10_cats_words_test.csv'
types = 0: '消极', 1: '积极'
pd_train = pd.read_csv(train_path)
pd_test = pd.read_csv(test_path)
print('训练集数目（总体）：%d' % pd_train.shape[0])
print('测试集数目（总体）：%d' % pd_test.shape[0])

for line in range(len(pd_train)):
    dict_cat = pd_train['cat'][line]
    dict_label = pd_train['label'][line]
    dict_content = str(pd_train['review'][line])
    train_cat.append(dict_cat)
    train_label.append(dict_label)
    train_review.append(dict_content)
print(len(train_cat),len(train_label),len(train_review))
print(train_cat[:5])
print(train_label[:5])

for line in range(len(pd_test)):
    dict_cat = pd_test['cat'][line]
    dict_label = pd_test['label'][line]
    dict_content = str(pd_test['review'][line])
    test_cat.append(dict_cat)
    test_label.append(dict_label)
    test_review.append(dict_content)
print(len(test_cat),len(test_label),len(test_review),"\\n")

#-----------------------------------------------------------------------------
#TFIDF计算
#将文本中的词语转换为词频矩阵 矩阵元素a[i][j]表示词j在第i类文本下的词频
vectorizer = CountVectorizer(min_df=10)   #MemoryError控制参数

#统计每个词语的tf-idf权值
transformer = TfidfTransformer()

#第一个fit_transform是计算tf-idf 第二个fit_transform是将文本转为词频矩阵
tfidf = transformer.fit_transform(vectorizer.fit_transform(train_review+test_review))
for n in tfidf[:5]:
    print(n)
print(type(tfidf))

#获取词袋模型中的所有词语  
word = vectorizer.get_feature_names()
print("单词数量:", len(word))

#元素w[i][j]表示词j在第i类文本中的tf-idf权重
X = coo_matrix(tfidf, dtype=np.float32).toarray()  #稀疏矩阵
print(X.shape)
print(X[:10])

X_train = X[:len(train_label)]
X_test = X[len(train_label):]
y_train = train_label
y_test = test_label
print(len(X_train),len(X_test),len(y_train),len(y_test))

#-----------------------------------------------------------------------------
#分类模型
clf = svm.LinearSVC()
print(clf)
clf.fit(X_train, y_train)
pre = clf.predict(X_test)
print('模型的准确度:'.format(clf.score(X_test, y_test)))
print(len(pre), len(y_test))
print(classification_report(y_test, pre, digits=4))
with open("SVM-pre-result.txt","w") as f:  #结果保存
    for v in pre:
        f.write(str(v)+"\\n")

其他常见机器学习模型如下：

clf = LogisticRegression(solver=‘liblinear’)
clf = RandomForestClassifier(n_estimators=5)
clf = DecisionTreeClassifier()
clf = AdaBoostClassifier()
clf = MultinomialNB()
clf = neighbors.KNeighborsClassifier(n_neighbors=3)

四.CNN和TextCNN情感分析

1.CNN

基本流程：

数据读取及预处理
特征提取
Embedding
构建深度学习分类模型
实验评估

训练构建的模型如下所示：

训练输出的结果如下：

Train on 40000 samples, validate on 10000 samples
Epoch 1/12
40000/40000 [==============================] - 8s 211us/step - loss: 0.4246 - acc: 0.8048 - val_loss: 0.3602 - val_acc: 0.8814
Epoch 2/12
40000/40000 [============================MATLAB实战系列（三十五）-MATLAB基于BP神经网络的光伏发电太阳辐照度预测
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[Python人工智能] 三十五.基于Transformer的商品评论情感分析 机器学习和深度学习的Baseline模型实现