From b9ca767c7956e0a760cc46a9690b72b7641bc93c Mon Sep 17 00:00:00 2001
From: pfc8hp2r6 <2317678682@qq.com>
Date: Mon, 16 Sep 2024 18:21:11 +0800
Subject: [PATCH] ADD file via upload

---
 fujia.py | 156 +++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 156 insertions(+)
 create mode 100644 fujia.py

diff --git a/fujia.py b/fujia.py
new file mode 100644
index 0000000..e689eed
--- /dev/null
+++ b/fujia.py
@@ -0,0 +1,156 @@
+import requests
+from urllib.parse import urlencode
+from pyquery import PyQuery as pq
+import time
+import xlwt
+import jieba
+from wordcloud import WordCloud
+import matplotlib.pyplot as plt
+from collections import Counter
+
+# 设置代理等（新浪微博的数据是用ajax异步下拉加载的，network->xhr）
+host = 'm.weibo.cn'
+base_url = f'https://{host}/api/container/getIndex?'
+
+# 设置请求头
+headers = {
+    'Host': host,
+    'Referer': 'https://m.weibo.cn/search?containerid=231522type%3D1%26q%3D%23%E7%BE%8E%E5%9B%BD%E7%96%AB%E6%83%85%23',
+    'User-Agent': 'Mozilla/5.0 (Linux; Android 5.0; SM-G900P Build/LRX21T) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/85.0.4183.121 Mobile Safari/537.36'
+}
+
+# 按页数抓取数据
+def get_single_page(page):
+    """
+    获取单个页面的数据。
+
+    :param page: 页面编号
+    :return: JSON 数据
+    """
+    params = {
+        'containerid': '231522type=1&q=#人工智能#',
+        'page_type': 'searchall',
+        'page': page
+    }
+    url = base_url + urlencode(params)
+    try:
+        response = requests.get(url, headers=headers)
+        if response.status_code == 200:
+            return response.json()
+    except requests.ConnectionError as e:
+        print('抓取错误:', e.args)
+
+# 解析页面返回的JSON数据
+count = 0
+
+def parse_page(json_data):
+    """
+    解析页面返回的JSON数据。
+
+    :param json_data: JSON 数据
+    :return: 解析后的数据
+    """
+    global count
+    items = json_data.get('data').get('cards')
+    for item in items:
+        mblog = item.get('mblog')
+        if mblog:
+            data = {
+                'id': mblog.get('id'),
+                'created': mblog.get('created_at'),
+                'text': pq(mblog.get('text')).text()  # 提取内容中的文本
+            }
+            yield data
+            count += 1
+
+def segment_text(text):
+    """
+    对文本进行分词处理。
+
+    :param text: 输入文本
+    :return: 分词后的列表
+    """
+    seg_list = jieba.cut(text.strip())
+    return list(seg_list)
+
+def generate_wordcloud(words):
+    """
+    生成词云图。
+
+    :param words: 分词后的单词列表
+    """
+    wordcloud = WordCloud(
+        font_path='simhei.ttf',  # 指定字体路径
+        background_color='white',
+        width=800,
+        height=600
+    ).generate(' '.join(words))
+
+    # 显示词云图
+    plt.imshow(wordcloud, interpolation='bilinear')
+    plt.axis('off')
+    plt.show()
+
+    # 保存词云图
+    wordcloud.to_file('fjt.png')
+
+def predict_event_probability(words, event_keywords):
+    """
+    根据关键词预测事件发生的概率。
+
+    :param words: 分词后的单词列表
+    :param event_keywords: 事件关键词列表
+    :return: 事件发生的概率
+    """
+    word_count = Counter(words)
+    total_words = sum(word_count.values())
+    event_word_count = sum(word_count[word] for word in event_keywords if word in word_count)
+    probability = event_word_count / total_words if total_words > 0 else 0
+    return probability
+
+if __name__ == '__main__':
+    workbook = xlwt.Workbook(encoding='utf-8')
+    worksheet = workbook.add_sheet('人工智能')
+
+    # 设置列标题
+    worksheet.write(0, 0, '创建时间')
+    worksheet.write(0, 1, '文本')
+    worksheet.write(0, 2, '分词结果')
+    worksheet.write(0, 3, '事件概率')
+
+    row = 1
+
+    all_words = []
+    event_keywords = ['人工智能']
+
+    for page in range(1, 5):
+        json_data = get_single_page(page)
+        results = parse_page(json_data)
+        tmp_list = []
+
+        for result in results:
+            created = result.get('created').strip('\n')
+            text = result.get('text').strip('\n')
+            segmented_text = segment_text(text)
+
+            # 写入创建时间和文本
+            worksheet.write(row, 0, label=created)
+            worksheet.write(row, 1, label=text)
+
+            # 写入分词结果
+            for idx, word in enumerate(segmented_text):
+                worksheet.write(row + idx, 2, label=word)
+
+            all_words.extend(segmented_text)  # 添加分词结果到列表
+
+            # 计算事件概率
+            event_probability = predict_event_probability(segmented_text, event_keywords)
+            worksheet.write(row, 3, label=event_probability)
+
+            row += len(segmented_text)  # 更新行号
+
+        time.sleep(1)  # 爬取时间间隔
+        workbook.save('fjt.xls')
+
+    # 生成词云图
+    generate_wordcloud(all_words)