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@@ -1,129 +1,4 @@
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Negative_Sentiment_Knowledge_Map_Class_Weekly.py

@@ -0,0 +1,180 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[22]:
+
+
+from pyvis.network import Network
+import pandas as pd
+
+
+# In[23]:
+
+
+path = "/Users/stephanielin/Desktop/TLTLab/"
+
+
+# In[24]:
+
+
+keyword_category = pd.read_excel( "assets/dictionary 2.0.xlsx",index_col=0).iloc[:,0:1]
+
+
+# In[25]:
+
+
+keywords = pd.read_excel("assets/dictionary 2.0.xlsx")["traget n-gram"].tolist()
+
+
+# In[26]:
+
+
+negative_file_names_2021 = ['Student1_Negative_SA','Student2_Negative_SA','Student3_Negative_SA','Student4_Negative_SA','Student5_Negative_SA','Student6_Negative_SA','Student7_Negative_SA']
+year2021_df = []
+for i in range(len(negative_file_names_2021)):
+    temp_df = pd.read_csv("Sentiment_Map/Negative_Map/2021/"+negative_file_names_2021[i]+".csv",index_col=0)
+    temp_df.reset_index(drop=True, inplace=True)
+    year2021_df.append(temp_df)
+
+
+# In[27]:
+
+
+student_names_2021 = ['Student1','Student2','Student3','Student4','Student5','Student6','Student7']
+
+
+# In[28]:
+
+
+negative_file_names_2022 = ['Student1_Negative_SA','Student2_Negative_SA','Student3_Negative_SA','Student4_Negative_SA','Student5_Negative_SA','Student6_Negative_SA','Student7_Negative_SA','Student8_Negative_SA','Student9_Negative_SA','Student10_Negative_SA','Student11_Negative_SA','Student12_Negative_SA']
+year2022_df = []
+for i in range(len(negative_file_names_2022)):
+    temp_df = pd.read_csv("Sentiment_Map/Negative_Map/2022/"+negative_file_names_2022[i]+".csv",index_col=0)
+    temp_df.reset_index(drop=True, inplace=True)
+    year2022_df.append(temp_df)
+
+
+# In[29]:
+
+
+student_names_2022 = ['Student1','Student2','Student3','Student4','Student5','Student6','Student7','Student8','Student9','Student10','Student11','Student12']
+
+
+# In[30]:
+
+
+negative_file_names_2023 = ['Student1_Negative_SA','Student2_Negative_SA','Student3_Negative_SA','Student4_Negative_SA','Student5_Negative_SA','Student6_Negative_SA','Student7_Negative_SA','Student8_Negative_SA','Student9_Negative_SA','Student10_Negative_SA','Student11_Negative_SA','Student12_Negative_SA']
+year2023_df = []
+for i in range(len(negative_file_names_2023)):
+    temp_df = pd.read_csv("Sentiment_Map/Negative_Map/2023/"+negative_file_names_2023[i]+".csv",index_col=0)
+    temp_df.reset_index(drop=True, inplace=True)
+    year2023_df.append(temp_df)
+
+
+# In[31]:
+
+
+student_names_2023 = ['Eury','Sadia','Helen','Xichen','Zhanlan','Katie','Andrea','Ana Maria','Heidi','Mariana','Inara','Kiki']
+
+
+# In[32]:
+
+
+# the number shows which category the keyword belongs to
+keywords_group = keyword_category.iloc[:, 0].to_numpy()
+
+
+# In[33]:
+
+
+category_color = []
+for i in keywords_group:
+    if i == 1:
+        category_color.append('#e3342f')
+    if i == 2:
+        category_color.append('#f6993f')
+    if i == 3:
+        category_color.append('#ffed4a')
+    if i == 4:
+        category_color.append('#38c172')
+    if i == 5:
+        category_color.append('#4dc0b5')
+    if i == 6:
+        category_color.append('#3490dc')
+    if i == 7:
+        category_color.append('#6574cd')
+    if i == 8:
+        category_color.append('#9561e2')
+    if i == 9:
+        category_color.append('#f66d9b')
+
+
+# In[34]:
+
+
+def class_collective_negative_sensitive_map(class_df, student_names_year):
+    nets = []
+    for i in range(class_df[0].shape[0]):
+        net = Network(notebook=True)
+        student_names = []
+        occurence = []
+
+        for n in range(len(keywords)):
+            student_names.append([])
+            occurence.append(0)
+        for number, student_df in enumerate(class_df):
+            for s, value in enumerate(student_df.iloc[i]):
+                if value == 1:
+                    #student_names[s].append("student"+str(number+1))
+                    student_names[s].append(student_names_year[number])
+            occurence = [sum(x) for x in zip(occurence, student_df.iloc[i])]
+        for student_df in class_df:
+            for p, value in enumerate(student_df.iloc[i]):
+                if value == 1:
+                    net.add_node(p, label=keywords[p], title='unset',size=(int(occurence[p])+1)*3, color=category_color[p])
+
+        # add names of student who use the knowledge to corresponding knowledge node's tittle in the map
+        for node in net.nodes:
+            node['title'] = ', '.join(student_names[int(node['id'])])
+
+        net_id = [dic['id'] for dic in net.nodes]
+
+        for o in net_id:
+            for u in net_id:
+                if o != u and keywords_group[o] == keywords_group[u]:
+                    net.add_edge(o,u,hidden=True)
+
+        net.repulsion(central_gravity=0.9,spring_length=50)
+
+        nets.append(net)
+
+    return nets
+
+
+# In[35]:
+
+
+class_negative_map_2021 = class_collective_negative_sensitive_map(year2021_df,student_names_2021)
+class_negative_map_2022 = class_collective_negative_sensitive_map(year2022_df,student_names_2022)
+class_negative_map_2023 = class_collective_negative_sensitive_map(year2023_df,student_names_2023)
+
+
+# In[36]:
+
+
+#class_negative_map_2023[0].show("social_network.html")
+
+
+# In[37]:
+
+
+class_negative_map_2023[0].nodes
+
+
+
+for i in range(len(class_negative_map_2023)):
+    class_negative_map_2023[i].show(f"assets/class_negative_map_2023_{i+1}.html")
+
+
+
+