# -*- coding: utf-8 -*-
# 代码12-1 评论去重的代码
import pandas as pd
import re
import jieba.posseg as psg
import numpy as np
# 去重,去除完全重复的数据
reviews = pd.read_csv("C:/Users/admin/Downloads/data/reviews.csv")
reviews = reviews[['content', 'content_type']].drop_duplicates()
content = reviews['content']
# 代码12-2 数据清洗
# 去除去除英文、数字等
# 由于评论主要为京东美的电热水器的评论,因此去除这些词语
strinfo = re.compile('[0-9a-zA-Z]|京东|美的|电热水器|热水器|')
content = content.apply(lambda x: strinfo.sub('', x))
# 代码12-3 分词、词性标注、去除停用词代码
# 分词
worker = lambda s: [(x.word, x.flag) for x in psg.cut(s)] # 自定义简单分词函数
seg_word = content.apply(worker)
# 将词语转为数据框形式,一列是词,一列是词语所在的句子ID,最后一列是词语在该句子的位置
n_word = seg_word.apply(lambda x: len(x)) # 每一评论中词的个数
n_content = [[x+1]*y for x,y in zip(list(seg_word.index), list(n_word))]
index_content = sum(n_content, []) # 将嵌套的列表展开,作为词所在评论的id
seg_word = sum(seg_word, [])
word = [x[0] for x in seg_word] # 词
nature = [x[1] for x in seg_word] # 词性
content_type = [[x]*y for x,y in zip(list(reviews['content_type']), list(n_word))]
content_type = sum(content_type, []) # 评论类型
result = pd.DataFrame({"index_content":index_content,
"word":word,
"nature":nature,
"content_type":content_type})
# 删除标点符号
result = result[result['nature'] != 'x'] # x表示标点符号
# 删除停用词
stop_path = open("C:/Users/admin/Downloads/data/stoplist.txt", 'r',encoding='UTF-8')
stop = stop_path.readlines()
stop = [x.replace('\n', '') for x in stop]
word = list(set(word) - set(stop))
result = result[result['word'].isin(word)]
# 构造各词在对应评论的位置列
n_word = list(result.groupby(by = ['index_content'])['index_content'].count())
index_word = [list(np.arange(0, y)) for y in n_word]
index_word = sum(index_word, []) # 表示词语在改评论的位置
# 合并评论id,评论中词的id,词,词性,评论类型
result['index_word'] = index_word
# 代码12-4 提取含有名词的评论
# 提取含有名词类的评论
ind = result[['n' in x for x in result['nature']]]['index_content'].unique()
result = result[[x in ind for x in result['index_content']]]
# 代码12-5 绘制词云
import matplotlib.pyplot as plt
from wordcloud import WordCloud
frequencies = result.groupby(by = ['word'])['word'].count()
frequencies = frequencies.sort_values(ascending = False)
backgroud_Image=plt.imread('C:/Users/admin/Downloads/data/pl.jpg')
wordcloud = WordCloud(font_path=r"C:/Windows/Fonts/Arial.ttf",
max_words=100,
background_color='white',
mask=backgroud_Image)
my_wordcloud = wordcloud.fit_words(frequencies)
plt.imshow(my_wordcloud)
plt.axis('off')
plt.show()
print('okkkkkk')
# 将结果写出
result.to_csv("C:/Users/admin/Downloads/data/word1.csv", index = False, encoding = 'utf-8')
print('11111111111')
import numpy as np
# 分词
worker = lambda s: [(x.word, x.flag) for x in psg.cut(s)] # 自定义简单分词函数
seg_word = content.apply(worker)
# 将词语转为数据框形式,一列是词,一列是词语所在的句子ID,最后一列是词语在该句子的位置
n_word = seg_word.apply(lambda x: len(x)) # 每一评论中词的个数
n_content = [[x+1]*y for x,y in zip(list(seg_word.index), list(n_word))]
index_content = sum(n_content, []) # 将嵌套的列表展开,作为词所在评论的id
seg_word = sum(seg_word, [])
word = [x[0] for x in seg_word] # 词
nature = [x[1] for x in seg_word] # 词性
content_type = [[x]*y for x,y in zip(list(reviews['content_type']),
list(n_word))]
content_type = sum(content_type, []) # 评论类型
result = pd.DataFrame({"index_content":index_content,
"word":word,
"nature":nature,
"content_type":content_type})
# 删除标点符号
result = result[result['nature'] != 'x'] # x表示标点符号
# 删除停用词
stop_path = open(r"C:\Users\admin\Downloads\data\stoplist.txt", 'r',encoding='UTF-8')
stop = stop_path.readlines()
stop = [x.replace('\n', '') for x in stop]
word = list(set(word) - set(stop))
result = result[result['word'].isin(word)]
# 构造各词在对应评论的位置列
n_word = list(result.groupby(by = ['index_content'])['index_content'].count())
index_word = [list(np.arange(0, y)) for y in n_word]
index_word = sum(index_word, []) # 表示词语在改评论的位置
# 合并评论id,评论中词的id,词,词性,评论类型
result['index_word'] = index_word
# 代码12-4
# 提取含有名词类的评论
ind = result[['n' in x for x in result['nature']]]['index_content'].unique()
result = result[[x in ind for x in result['index_content']]]
# 代码12-5
import matplotlib.pyplot as plt
from wordcloud import WordCloud
frequencies = result.groupby(by = ['word'])['word'].count()
frequencies = frequencies.sort_values(ascending = False)
backgroud_Image=plt.imread(r'C:\Users\admin\Downloads\data/pl.jpg')
wordcloud = WordCloud(font_path=r"C:/Windows/Fonts/Arial.ttf",
max_words=100,
background_color='white',
mask=backgroud_Image)
my_wordcloud = wordcloud.fit_words(frequencies)
plt.imshow(my_wordcloud)
plt.axis('off')
plt.show()
# 将结果写出
result.to_csv("../word1.csv", index = False, encoding = 'utf-8')
# -*- coding: utf-8 -*- # 代码12-6 匹配情感词 import pandas as pd import numpy as np word = pd.read_csv("../word1.csv") # 读入正面、负面情感评价词 pos_comment = pd.read_csv(r"C:/Users/admin/Downloads/data/正面评价词语(中文).txt", header=None,sep="/n", encoding = 'utf-8', engine='python') neg_comment = pd.read_csv(r"C:/Users/admin/Downloads/data/负面评价词语(中文).txt", header=None,sep="/n", encoding = 'utf-8', engine='python') pos_emotion = pd.read_csv(r"C:/Users/admin/Downloads/data/正面情感词语(中文).txt", header=None,sep="/n", encoding = 'utf-8', engine='python') neg_emotion = pd.read_csv(r"C:/Users/admin/Downloads/data/负面情感词语(中文).txt", header=None,sep="/n", encoding = 'utf-8', engine='python') # 合并情感词与评价词 positive = set(pos_comment.iloc[:,0])|set(pos_emotion.iloc[:,0]) negative = set(neg_comment.iloc[:,0])|set(neg_emotion.iloc[:,0]) intersection = positive&negative # 正负面情感词表中相同的词语 positive = list(positive - intersection) negative = list(negative - intersection) positive = pd.DataFrame({"word":positive, "weight":[1]*len(positive)}) negative = pd.DataFrame({"word":negative, "weight":[-1]*len(negative)}) posneg = positive.append(negative) # 将分词结果与正负面情感词表合并,定位情感词 data_posneg = posneg.merge(word, left_on = 'word', right_on = 'word', how = 'right') data_posneg = data_posneg.sort_values(by = ['index_content','index_word']) # 代码12-7 修正情感倾向 # 根据情感词前时候有否定词或双层否定词对情感值进行修正 # 载入否定词表 notdict = pd.read_csv("C:/Users/admin/Downloads/data/not.csv") # 处理否定修饰词 data_posneg['amend_weight'] = data_posneg['weight'] # 构造新列,作为经过否定词修正后的情感值 data_posneg['id'] = np.arange(0, len(data_posneg)) only_inclination = data_posneg.dropna() # 只保留有情感值的词语 only_inclination.index = np.arange(0, len(only_inclination)) index = only_inclination['id'] for i in np.arange(0, len(only_inclination)): review = data_posneg[data_posneg['index_content'] == only_inclination['index_content'][i]] # 提取第i个情感词所在的评论 review.index = np.arange(0, len(review)) affective = only_inclination['index_word'][i] # 第i个情感值在该文档的位置 if affective == 1: ne = sum([i in notdict['term'] for i in review['word'][affective - 1]]) if ne == 1: data_posneg['amend_weight'][index[i]] = -\ data_posneg['weight'][index[i]] elif affective > 1: ne = sum([i in notdict['term'] for i in review['word'][[affective - 1, affective - 2]]]) if ne == 1: data_posneg['amend_weight'][index[i]] = -\ data_posneg['weight'][index[i]] # 更新只保留情感值的数据 only_inclination = only_inclination.dropna() # 计算每条评论的情感值 emotional_value = only_inclination.groupby(['index_content'], as_index=False)['amend_weight'].sum() # 去除情感值为0的评论 emotional_value = emotional_value[emotional_value['amend_weight'] != 0] # 代码12-8 查看情感分析效果 # 给情感值大于0的赋予评论类型(content_type)为pos,小于0的为neg emotional_value['a_type'] = '' emotional_value['a_type'][emotional_value['amend_weight'] > 0] = 'pos' emotional_value['a_type'][emotional_value['amend_weight'] < 0] = 'neg' # 查看情感分析结果 result = emotional_value.merge(word, left_on = 'index_content', right_on = 'index_content', how = 'left') result = result[['index_content','content_type', 'a_type']].drop_duplicates() confusion_matrix = pd.crosstab(result['content_type'], result['a_type'], margins=True) # 制作交叉表 (confusion_matrix.iat[0,0] + confusion_matrix.iat[1,1])/confusion_matrix.iat[2,2] # 提取正负面评论信息 ind_pos = list(emotional_value[emotional_value['a_type'] == 'pos']['index_content']) ind_neg = list(emotional_value[emotional_value['a_type'] == 'neg']['index_content']) posdata = word[[i in ind_pos for i in word['index_content']]] negdata = word[[i in ind_neg for i in word['index_content']]] # 绘制词云 import matplotlib.pyplot as plt from wordcloud import WordCloud # 正面情感词词云 freq_pos = posdata.groupby(by = ['word'])['word'].count() freq_pos = freq_pos.sort_values(ascending = False) backgroud_Image=plt.imread('C:/Users/admin/Downloads/data/pl.jpg') wordcloud = WordCloud(font_path="C:/Windows/Fonts/Arial.ttf", max_words=100, background_color='white', mask=backgroud_Image) pos_wordcloud = wordcloud.fit_words(freq_pos) plt.imshow(pos_wordcloud) plt.axis('off') plt.show() # 负面情感词词云 freq_neg = negdata.groupby(by = ['word'])['word'].count() freq_neg = freq_neg.sort_values(ascending = False) neg_wordcloud = wordcloud.fit_words(freq_neg) plt.imshow(neg_wordcloud) plt.axis('off') plt.show() # 将结果写出,每条评论作为一行 posdata.to_csv("C:/Users/admin/Downloads/data/posdata.csv", index = False, encoding = 'utf-8') negdata.to_csv("C:/Users/admin/Downloads/data/negdata.csv", index = False, encoding = 'utf-8')
标签:数据分析,index,word,type,content,评论,result,data From: https://www.cnblogs.com/3045qqq/p/17283440.html