def k_means_mine(o_weight, o_features, k):
"""
完成K-Means聚类，把同类的词语分在一起，分别保存为临时文件
:param o_weight: 词权重
:param o_features: 特征词
:param k: 簇类
:return: null
"""
weight, features, cluster = o_weight, o_features, k
print('================== KMenas ====================')
centroids, index_distance = my_k_means(weight, cluster)
tmp = centroids.argsort()[:, ::-1]
for i in range(cluster):
print("cluster %d:" % i, end='')
tmp_words_txt = open("./data/tmp4kmeans/Cluster{}.txt".format(i), "w", encoding='utf-8')
for j in tmp.A[i, :50]:
print(' %s ' % features[j], end='')
tmp_words_txt.write(features[j] + '\n')
tmp_words_txt.close()
print()
print('需要词云处理的kmeans临时文件已经生成，\n正在等待可视化模块的调用，请稍候...')
clear_all_var()
return
def analysis(start_time, end_time, topic_id, k):
"""
统合前面的所有函数
:param start_time: 开始时间
:param end_time: 结束时间
:param topic_id: 话题ID
:param k: 簇类
:return: 作为其他函数处理的标志
"""
start = time.time()
data = connect_mysql(start_time, end_time, topic_id)
try:
print(data['CONTENT'][0])
except Exception:
print('您所查日期内并没有值，告辞')
return 0
collector = jieba_cut(data)
x, y = tf_idf(collector)
k_means_mine(x, y, k)
# 根据词频生成词云
wc4tf_idf(topic_id, k)
# 根据KMeans生成词云
wc4k_means(topic_id, k)
print("================== 词云已经生成 ====================")
end = time.time()
print("分析结束！运行时间: %f" % (end - start) + 's')
clear_all_var()
return 1
def get_stop_words():
"""
读取data下setup中的自定义数据
"""
stop_words = set()
stop_words_file = open('./data/setup/word_cloud_stop_words.txt',
encoding='utf-8')
for tt in stop_words_file.readlines():
stop_words.add(tt.strip('\n'))
stop_words_file.close()
clear_all_var()
def get_abstract(data, a, b, c):
"""
生成excel可以打开的的摘要文件
:param data: MySQL从取出的数据
:param a: 起始时间的
:param b: 结束时间
:param c: 话题ID
:return: null
"""
now_path = os.getcwd()
path = now_path.replace('\\', '/')
tr4s = TextRank4Sentence()
print('当前文章的摘要：')
results = []
for i in range(len(data['CONTENT'])):
# i = re.sub("[^\u4e00-\u9fa5]", '', i) # 记住只留文本？没有断句算不上摘要，这里需要用其他方式处理
# print('\u3002 \uff1b \uff0c \uff1a \u201c \u201d'
# '\uff08 \uff09 \u3001 \uff1f \u300a \u300b')
# # 。 ； ， ： “ ”（ ） 、 ？ 《 》
tmp = re.sub(
"[^\u4e00-\u9fa5\u3002\uff1b\uff0c\uff1a\u201c\u201d\uff08\uff09\u3001\uff1f\u300a\u300b0-9]",
'', data['CONTENT'][i])
tr4s.analyze(text=tmp, lower=True)
result = ''
# print()
# print('摘要：')
for item in tr4s.get_key_sentences(num=3):
# print(item.index, item.weight, item.sentence)
result += item.sentence
if len(result) != 0:
results.append([
data['UPTIME'][i], data['TITLE'][i], data['AUTHOR'][i], result
])
# data['CONTENT'][i] = results
column_name = ['更新时间', '标题/题目', '作者', '摘要']
tmp_text = pd.DataFrame(columns=column_name, data=results)
tmp_text.to_csv('./data/textrank/topic{}_{}-{}abstract.csv'.format(
c, a, b),
encoding='utf_8_sig')
print('>>>>>>>>>>>>>> 已经保存到csv等待计算或查看 >>>>>>>>>>>>>>>')
# os.startfile(now_path + '/data/textrank/topic{}_{}-{}abstract.csv'.format(c, a, b)) # 弹出工作表
# 但是这里需要完整的工作路径才行 T:/AC/Python/PublicOpinionMonitor/data/textrank/topic{}_abstract.csv
# print(results)
# print(' >>>>>> >>>>>>>>> 将在10秒后关闭excel >>>>>>> >>>>>>>> ')
time.sleep(10)
clear_all_var()
return
def wc4k_means(topic_id, k):
"""
主要根据临时文件的数据，在固定大小的情况下，先单独一张张地生成图片，再拼接起来
:param topic_id:
:param k:
"""
tmp_width = int(1920 / k)
for i in range(k):
words = set()
for line in open('./data/tmp4kmeans/Cluster{}.txt'.format(i),
'r',
encoding='utf-8'):
words.add(line.strip('\n'))
words = ' '.join(words) # <class 'str'>
wc = WordCloud(font_path="./data/msyh.ttc",
max_words=100,
width=tmp_width,
height=1080,
background_color='white',
stopwords=get_stop_words(),
colormap=get_color())
word_cloud = wc.generate(words)
# 写词云图片
word_cloud.to_file("./data/wordcloud/wc_k_means_topic{}_{}.png".format(
topic_id, i))
# 显示词云文件
plt.imshow(word_cloud)
plt.axis("off")
# plt.show()
print("第 {}/{} 张分图已经切片完成".format(i + 1, k))
print('开始拼图')
image_collector = []
for i in range(k):
tmp = Image.open('./data/wordcloud/wc_k_means_topic{}_{}.png'.format(
topic_id, i))
image_collector.append(tmp)
width, height = image_collector[0].size
result = Image.new(image_collector[0].mode,
(width * len(image_collector), height))
for i, im in enumerate(image_collector):
result.paste(im, box=(i * width, 0))
result.save(
'./data/wordcloud/wc_k_means_topic{}_result.png'.format(topic_id))
print("图片拼接完成，正在弹出图片，请稍候...")
# Image.open('./data/wordcloud/wc_k_means_topic{}_result.png'.format(topic_id)).show()
clear_all_var()
def html_report(a, b, c):
"""
统合前面的函数并组装HTML报告
:param a: 开始时间
:param b: 结束时间
:param c: 话题id
:return:
"""
x, y = get_tpm2(a, b, c)
print(a, b, c, type(a), type(b), type(c))
data = get_tpm(a, b, c)
page = Page(layout=Page.SimplePageLayout, page_title='舆情分析统计报告')
page.add(
scatter_sentiment(x, y),
pie_html(data),
)
page.render("./data/reportforms/report_topic{}_{}-{}.html".format(c, a, b))
print('================== 动态网页组装完成,已保存 ==================')
sao_operation = '''
<h3 style="text-align:center">TF-IDF 关键词/主旨词汇提取</h1>
<p style="text-align:center"><img src="../wordcloud/wc_tf_idf_topic{}.png" width=85% /></p>
<h3 style="text-align:center">K-Means 聚类词汇展示</h2>
<p style="text-align:center"><img src="../wordcloud/wc_k_means_topic{}_result.png" width=85% /></p>
<h3 style="text-align:center">其他 资料</h2>
<p style="text-align:center"><a href="../textrank/topic{}_{}-{}abstract.csv">点击打开您查询日期内的csv摘要文件</a></p>
<p style="text-align:center"> 注： 以上所有报告均已去除无效值（仅含文本数据) </p>
</body>
</html>
'''.format(c, c, c, a, b)
with open('./data/reportforms/report_topic{}_{}-{}.html'.format(c, a, b), 'r', encoding='utf-8') as h:
content = h.read()
content = content.replace('</body>\n</html>', '')
with open('./data/reportforms/report_topic{}_{}-{}.html'.format(c, a, b), 'w', encoding='utf-8') as h:
tmp = content + sao_operation
h.write(tmp)
now_path = os.getcwd()
path = now_path.replace('\\', '/')
os.startfile(path + '/data/reportforms/report_topic{}_{}-{}.html'.format(c, a, b))
clear_all_var()
return
def analysis_sentiment(a, b, c):
"""
在摘要中追加注入感情的倾向值
:param a: 开始时间
:param b: 结束
:param c: 话题ID
:return: null
"""
# 防读取错误
time.sleep(3)
cc = os.system('tasklist|find /i "excel.exe"')
while cc == 0:
os.system('taskkill /IM excel.exe')
time.sleep(3)
print('>>>> 正在尝试关闭了已经打开的csv文件， >>>>> >>>> >>>>')
cc = os.system('tasklist|find /i "excel.exe"')
else:
print('>>>> 不存在打开的csv文件，开始执行情感分析 >>>>> >>>> >>>>')
pass
csv_file = './data/textrank/topic{}_{}-{}abstract.csv'.format(c, a, b)
csv_data = pd.read_csv(csv_file, low_memory=False) # 防止弹出警告
# 情感分析
results = []
for i in csv_data['摘要']:
s = SnowNLP(i)
results.append(s.sentiments)
csv_data['sentiment'] = results
csv_data.to_csv('./data/textrank/topic{}_{}-{}abstract.csv'.format(
c, a, b),
encoding='utf_8_sig',
index=False)
print('............ 情感值追加注入完成 ...........')
# 直接计算数据库的数据太过庞大，改为计算摘要的情感
clear_all_var()
return
def get_abs(start_time, end_time, topic_id):
"""
统合上面的所有功能
:param start_time: 开始时间
:param end_time: 结束时间
:param topic_id: 话题id
:return: null
"""
start = time.time()
tmp = data_get(start_time, end_time, topic_id)
get_abstract(tmp, start_time, end_time, topic_id)
# return
# for i in range(len(tmp['CONTENT'])):
# print(tmp['TITLE'][i])
# print(tmp['AUTHOR'][i])
# break # success!
# 注意：无法追加写入打开了的文件。请关闭后重试（直接在这里面关闭文件夹算了
analysis_sentiment(start_time, end_time, topic_id) # 内容为空的时候无法进行下一步计算
print(' 文本摘要和情感分析模块 消耗了 {} s'.format(time.time() - start))
clear_all_var()
return
def wc4tf_idf(topic_id, k):
"""
TF-IDF生成词云
:param topic_id:
:param k:
"""
frequencies = {}
for line in open('./data/tmp4tfidf/tmp4wordcloud.txt', encoding='utf-8'):
arr = line.split(" ")
frequencies[arr[0]] = float(arr[1])
wc = WordCloud(font_path="./data/msyh.ttc",
max_words=300,
width=1920,
height=1080,
background_color='white',
stopwords=get_stop_words())
word_cloud = wc.generate_from_frequencies(frequencies)
# 写词云图片
word_cloud.to_file(
"./data/wordcloud/wc_tf_idf_topic{}.png".format(topic_id))
# 显示词云文件
print('TF / IDF 词频图云已经完成，正在弹出图片，请稍候...')
# Image.open("./data/wordcloud/wc_tf_idf_topic{}.png".format(topic_id)).show()
clear_all_var()
def real_time_mode(self):
sys.stdout = EmittingStream(textWritten=self.hello)
# schedule.every(7).day.at('08:00').do(self.analysis_btn_click())
# schedule.every(5).minutes.do(self.analysis_btn_click())
key_words, start_time, end_time, k = self.blank_check()
if key_words == 0 and start_time == 0 and end_time == 0 and k == 0:
QMessageBox.about(self, '无法查询', '请输入关键字，关键字是必须的')
return
elif key_words == 1 and start_time == 1 and end_time == 1 and k == 1:
QMessageBox.about(self, '无法查询', '麻烦正确输入日期')
return
else:
pass
# print('实施模式下，为保证程序稳定性，只分析最近一个月的数据')
end_time = datetime.datetime.now().strftime('%Y%m%d')
if int(start_time) > int(end_time):
self.info.setText('本系统不支持查询未来')
return
search = SearchTerm(key_words)
# search.topic_id, note = get_topic_id(search.keywords, search.header)
# IP出现问题时的临时解决办法：单机指定：测试模式
# 19570564 武汉ID 19639859 境外ID 19622792 痛苦ID 19564412 恋爱ID
search.topic_id = "19564412"
note = 'IP或网站出现问题，临时解决措施：测试模式'
print(search.keywords, search.topic_id, note)
if self.checkBox.isChecked():
# print('实时模式：准备就绪\n为保证程序稳定性，建议只分析最近一个月的数据')
try:
global t # 准确的时间
t = Timer(1200, self.real_time_mode) # 5min # 推荐是按天算的，所以没有判断文件存在的选项，必定不一样
t.start()
# 测试模式：数据暂时无法获取
print("测试模式：使用本地数据，跳过数据获取")
# spider(search.keywords, search.topic_id, search.header)
time.sleep(5)
get_abs(start_time, end_time, search.topic_id)
time.sleep(5)
analysis(start_time, end_time, search.topic_id, k)
time.sleep(5)
html_report(start_time, end_time, search.topic_id)
time.sleep(5)
mail_report(start_time, end_time, search.topic_id)
time.sleep(5)
# self.query_btn_click() # 自动查询 TODO 注意这里是默认执行程序，需要更改
# self.analysis_btn_click() # 自动分析
clear_all_var()
except Exception:
print('本轮出现了一个错误，跳过\n请检查日期内数据是否存在')
# 看需不需要发送错误报告
finally:
# global t # 模糊的时间
# t = Timer(300, self.real_time_mode) # 5min # 推荐是按天算的，所以没有判断文件存在的选项，必定不一样
# t.start()
pass
else:
self.analysis_btn_click()
return
def analysis_btn_click(self):
key_words, start_time, end_time, k = self.blank_check()
if key_words == 0 and start_time == 0 and end_time == 0 and k == 0:
QMessageBox.about(self, '无法查询', '请输入关键字，关键字是必须的')
return
elif key_words == 1 and start_time == 1 and end_time == 1 and k == 1:
QMessageBox.about(self, '无法查询', '麻烦正确输入日期')
return
else:
pass
self.img_change0.setStyleSheet("image: url(./data/tmp4design/舆情监控红500.png);")
self.setWindowIcon(QIcon('./data/tmp4design/网络舆情红.png'))
sys.stdout = EmittingStream(textWritten=self.update_info)
search = SearchTerm(key_words)
# search.topic_id, note = get_topic_id(search.keywords, search.header)
# IP出现问题时的临时解决办法：单机指定：测试模式
# 19570564 武汉ID 19639859 境外ID 19575118 奋斗ID 19622792 痛苦ID
search.topic_id = "19622792"
note = 'IP或网站出现问题，临时解决措施：测试模式'
print(search.keywords, search.topic_id, note)
try:
print('================== 数据分析：开始 ====================')
cqc = analysis(start_time, end_time, search.topic_id, int(k))
if cqc == 0:
self.img_change0.setStyleSheet("image: url(./data/tmp4design/舆情监控蓝500.png);")
self.setWindowIcon(QIcon('./data/tmp4design/负面舆情帽子蓝.png'))
return
print('================== 聚类完成，图片生成 ====================')
time.sleep(3)
try:
html_report(start_time, end_time, search.topic_id)
except Exception:
print('================== 没有生成文摘，优先生成文摘 ====================')
time.sleep(5)
get_abs(start_time, end_time, search.topic_id)
finally:
html_report(start_time, end_time, search.topic_id)
print('================== 已经弹出了HTML报告 ====================')
time.sleep(3)
reply = QMessageBox.question(self, '来自邮件系统：',
"需要讲HTML发送到邮箱作为历史保存嘛？（建议发送）\n如要发送，请关闭相关文件", QMessageBox.Yes |
QMessageBox.No, QMessageBox.Yes)
if reply == QMessageBox.Yes:
try:
mail_report(start_time, end_time, search.topic_id)
print('================== 已发送相关文件 ====================')
except Exception:
print('发送失败，请检查')
else:
pass
self.update_info(' {} : {} 分析完成 请检查\n相关文件存放在data下面，如有需要请自行查看'.format(key_words, search.topic_id))
except Exception:
self.warning_message('有错误发生，请检查！\n建议增加单词分析的数据量（增大时间跨度）')
self.img_change0.setStyleSheet("image: url(./data/tmp4design/舆情监控蓝500.png);")
self.setWindowIcon(QIcon('./data/tmp4design/负面舆情帽子蓝.png'))
return
self.img_change0.setStyleSheet("image: url(./data/tmp4design/舆情监控蓝500.png);")
self.setWindowIcon(QIcon('./data/tmp4design/负面舆情帽子蓝.png'))
clear_all_var()
return
def mail_report(a, b, topic_id):
# 路径
now_path = os.getcwd()
path = now_path.replace('\\', '/')
x_code = "your code"
img1_path = path + "/data/wordcloud/wc_tf_idf_topic{}.png".format(topic_id)
img2_path = path + "/data/wordcloud/wc_k_means_topic{}_result.png".format(
topic_id)
csv_file = path + '/data/textrank/topic{}_{}-{}abstract.csv'.format(
topic_id, a, b)
html_file = path + '/data/reportforms/report_topic{}_{}-{}.html'.format(
topic_id, a, b)
content = MIMEMultipart()
image1 = MIMEImage(open(img1_path, 'rb').read(), _subtype='octet-stream')
image1.add_header('Content-Disposition', 'attachment', filename=img1_path)
content.attach(image1)
image2 = MIMEImage(open(img2_path, 'rb').read(), _subtype='octet-stream')
image2.add_header('Content-Disposition', 'attachment', filename=img2_path)
content.attach(image2)
csv1 = MIMEApplication(open(csv_file, 'rb').read())
csv1.add_header('Content-Disposition', 'attachment', filename=csv_file)
content.attach(csv1)
html1 = MIMEApplication(open(html_file, 'rb').read())
html1.add_header('Content-Disposition', 'attachment', filename=html_file)
content.attach(html1)
with open(img1_path, 'rb') as im1:
msg_image1 = MIMEImage(im1.read())
msg_image1.add_header('Content-ID', '<image1>')
content.attach(msg_image1)
with open(img2_path, 'rb') as im2:
msg_image2 = MIMEImage(im2.read())
msg_image2.add_header('Content-ID', '<image2>')
content.attach(msg_image2)
msg = '''
<h1>舆情分析简报：</h1>
<h2 style="color:red">This is a tf-idf：</h1>
<p><img src="cid:image1" width=75% /></p>
<h2 style="color:blue">This is a k-means：</h1>
<p><img src="cid:image2" width=75% /></p>
<p>如果想看详细报告，请手动下载附件4：HTML网页报告</p>
'''
message = MIMEText(msg, 'html', 'utf-8')
content.attach(message)
content['Subject'] = '舆情监控系统 汇报 20分钟定时 模式 {}'.format(
check_time(time.time()))
content['To'] = 'to [email protected]'
content['From'] = "from [email protected]"
server = smtplib.SMTP_SSL("smtp.qq.com", port=465)
try:
server.login("from [email protected]", x_code)
print('login success!!!')
server.sendmail("from [email protected]", 'to [email protected]',
content.as_string())
print('Send Successful!')
except Exception:
print(
'邮箱出现了一点异常，数据保存在文件夹下data文件中的reporforms中\n文件名为:report_topic{}_{}-{}.html'
.format(topic_id, a, b))
finally:
clear_all_var()
return
