【getTableData.py】情感分析接管数据的函数定义

@@ -152,3 +152,59 @@ def getCommentCharDataTwo():# 统计评论数据中不同性别的数量
         })
         })
     return resultData
     return resultData
 
 
+def getYuQingCharDataOne():# 统计热词中正面、中性、负面的数量
+    hotWordList = getAllHotWords()
+    xData = ['正面','中性','负面']
+    yData = [0,0,0]
+    for word in hotWordList:
+        emotionValue = SnowNLP(word[0]).sentiments
+        if emotionValue > 0.5:
+            yData[0] += 1
+        elif emotionValue == 0.5:
+            yData[1] += 1
+        elif emotionValue < 0.5:
+            yData[2] += 1
+    finalData = [{
+        'name':x,
+        'value':yData[index]
+    } for index,x in enumerate(xData)]
+    return xData,yData,finalData
+
+def getYuQingCharDataTwo():# 统计评论列表和文章列表中的情感值
+    xData = ['正面', '中性', '负面']
+    finalData1 = [{
+        'name':x,
+        'value':0
+    } for x in xData]
+    finalData2 = [{
+        'name': x,
+        'value': 0
+    } for x in xData]
+
+    for comment in commentList:
+        emotionValue = SnowNLP(comment[4]).sentiments
+        if emotionValue > 0.5:
+            finalData1[0]['value'] += 1
+        elif emotionValue == 0.5:
+            finalData1[1]['value'] += 1
+        elif emotionValue < 0.5:
+            finalData1[2]['value'] += 1
+    for artile in articleList:
+        emotionValue = SnowNLP(artile[5]).sentiments
+        if emotionValue > 0.5:
+            finalData2[0]['value'] += 1
+        elif emotionValue == 0.5:
+            finalData2[1]['value'] += 1
+        elif emotionValue < 0.5:
+            finalData2[2]['value'] += 1
+    return finalData1,finalData2
+
+def getYuQingCharDataThree():# 提取前10个热词及其对应的出现频率
+    hotWordList = getAllHotWords()
+    xData = []
+    yData = []
+    for i in hotWordList[:10]:
+        xData.append(i[0])
+        yData.append(int(i[1]))
+    return xData,yData
+

+from utils.getPublicData import getAllArticleData
+from snownlp import SnowNLP
+
+def getTableDataList(flag):
+    if flag:
+        tableList = []
+        articeList = getAllArticleData()
+        for article in articeList:
+            item = list(article)
+            value = ''
+            if SnowNLP(item[5]).sentiments > 0.5:
+                value = '正面'
+            elif SnowNLP(item[5]).sentiments < 0.5:
+                value = '负面'
+            else:
+                value = '中性'
+            item.append(value)
+            tableList.append(item)
+        return tableList
+    else:
+        return getAllArticleData()