舆情预测函数定义

utils/yuqingpredict.py

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+from utils.getPublicData import *
+articleList = getAllArticleData()
+commentList = getAllCommentsData()
+import csv
+import os
+import datetime
+def getTopicByArticle():# 返回文章内容的话题字典
+    articleTopicDic = {}
+    for i in articleList:
+        if i[14] != None:
+            if i[14] in articleTopicDic.keys():
+                articleTopicDic[i[14]] += 1
+            else:
+                articleTopicDic[i[14]] = 1
+    resultData = []
+    for key,value in articleTopicDic.items():
+        resultData.append({
+            'name':key,
+            'value':value
+        })
+    return resultData
+
+def getTopicByComments():# 返回评论内容的话题字典
+    commentsTopicDic = {}
+    for i in commentList:
+        if i[9] != None:
+            if i[9] in commentsTopicDic:
+                commentsTopicDic[i[9]] += 1
+            else:
+                commentsTopicDic[i[9]] = 1
+    resultData = []
+    for key,value in commentsTopicDic.items():
+        resultData.append({
+            'name':key,
+            'value':value
+        })
+    return resultData
+
+def mergeTopics(article_topics, comment_topics):# 合并话题
+    merged_dict = {}
+    for topic in article_topics + comment_topics:
+        if topic['name'] in merged_dict:
+            merged_dict[topic['name']] += topic['value']
+        else:
+            merged_dict[topic['name']] = topic['value']
+    merged_list = [{'name': key, 'value': value} for key, value in merged_dict.items()]
+    return merged_list
+def getTopicData():
+    # 读取合并文件 merge.csv    # 取前十个话题
+    top_10_topics = pd.read_csv('./merged_topics.csv').head(10)
+    # 获取话题名称和对应的值
+    xData = top_10_topics['name'].tolist()
+    yData = top_10_topics['value'].tolist()
+    return xData, yData
+
+def getTopicPageCreatedAtCharData(topic):# 统计特定话题的评论在每个日期的数量，并返回日期和对应的评论数量
+    createdAt = {}
+    for i in articleList:
+        if i[14]==topic:
+            if i[7] in createdAt.keys():
+                createdAt[i[7]] += 1
+            else:
+                createdAt[i[7]] = 1
+    for i in commentList:
+        if i[9]==topic:
+            if i[1] in createdAt.keys():
+                createdAt[i[1]] += 1
+            else:
+                createdAt[i[1]] = 1
+    sorted_data = {k: createdAt[k] for k in sorted(createdAt, key=lambda date: datetime.datetime.strptime(date, "%Y-%m-%d"))}
+    return topic,sorted_data
+    # return topic,list(createdAt.keys()),list(createdAt.values())
+    # return topic, createdAt.items()
+
+def writeTopicsToCSV(topics, file_name):
+    # 检查文件是否存在，如果存在则附加写入，否则新建一个
+    file_exists = os.path.isfile(file_name)
+    # 按值的降序排序
+    sorted_topics = sorted(topics, key=lambda x: x['value'], reverse=True)
+    with open(file_name, 'w', newline='', encoding='utf-8') as csvfile:
+        fieldnames = ['name', 'value']
+        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
+        # 如果文件不存在，则写入表头
+        if not file_exists:
+            writer.writeheader()
+        # 写入数据
+        for topic in sorted_topics:
+            writer.writerow(topic)
+if __name__ == '__main__':
+    # 将话题数据写入 CSV 文件
+    # merged_topics = mergeTopics(getTopicByArticle(), getTopicByComments())
+    # writeTopicsToCSV(merged_topics, 'merged_topics.csv')
+    print(getTopicPageCreatedAtCharData("生活"))