Merge branch 'master' into feature/sentiment_vector

Project/Tools/Emoji_Distance.py

@@ -0,0 +1,146 @@
+
+# coding: utf-8
+
+# # Emoji Distance
+# a notebook dealing witch emoji distance measures. Uses an external csv with labeled data to compare arbitriary emojis related to sentiment
+# Autor = Carsten Draschner
+# Version = 0.1
+# ## Used Ressources
+# https://www.clarin.si/repository/xmlui/handle/11356/1048
+# https://github.com/words/emoji-emotion
+
+# In[1]:
+
+
+import pandas as pd
+import math
+import numpy as np
+
+N=3
+# In[53]:
+
+
+#read in csv as panda file
+df = pd.read_csv("/Users/Carsten/GitRepos/NLP-LAB/Project/Tools/Emoji_Sentiment_Data_v1.0.csv", delimiter=";")
+#df.head()
+
+
+# In[54]:
+
+
+#calculates vector distance between 2 3-dim sentiment representations of emojis
+def sentiment_vector_dist(v1,v2):
+    #pos_v1 = v1[0]
+    #neg_v1 = v1[1]
+    #neu_v1 = v1[2]
+
+    #pos_v2 = v2[0]
+    #neg_v2 = v2[1]
+    #neu_v2 = v2[2]
+
+    #tmp_dist = float(np.abs(pos_v1-pos_v2))+float(np.abs(neg_v1-neg_v2))+float(np.abs(neu_v1-neu_v2))
+
+    #calculates vector distance between 2 3-dim sentiment representations of emojis consisting of positive neutral and negative probabilistic occuring
+    tmp_dist = np.linalg.norm(np.array(v1)-np.array(v2))
+    return tmp_dist
+
+
+# In[55]:
+
+
+#calculates vector representation in a 3dim 0 to 1space of dimension: positive,negative,neutral
+def emoji_to_sentiment_vector(e):
+    tmp = df[df["Emoji"]==e]
+    #calculate by espacial labeled occurences devided by sum of overall occurences
+    pos = tmp["Positive"].values[0]/tmp["Occurrences"].values[0]
+    neg = tmp["Negative"].values[0]/tmp["Occurrences"].values[0]
+    neu = tmp["Neutral"].values[0]/tmp["Occurrences"].values[0]
+    #return as np array
+    return np.array([pos,neg,neu])
+
+
+# In[56]:
+
+
+#function to call for evaluating two emojis in its sentimental distance
+def emoji_distance(e1,e2):
+    sent_v1 = emoji_to_sentiment_vector(e1)
+    sent_v2 = emoji_to_sentiment_vector(e2)
+
+    d = sentiment_vector_dist(sent_v1,sent_v2)
+    return d
+
+
+# In[57]:
+
+
+def sentiment_vector_to_emoji(v1):
+    #if(len(v1) == 3):
+        #set initial values to compare with
+        best_emoji = "😐"
+        min_distance = 10000
+
+        #compare only with filtred emoticons
+        df_filtered = df[df["Unicode block"]=="Emoticons"]
+        all_smilies = list(df_filtered["Emoji"])
+        for e in all_smilies:
+            v2 = emoji_to_sentiment_vector(e)
+            d = sentiment_vector_dist(v1,v2)
+            if(d < min_distance):
+                min_distance = d
+                best_emoji = e
+                #print(str(v1),str(v2),str(min_distance),str(type(v1)),str(type(v2)),e)
+
+
+        #print("for sentiment vector: "+str(v1)+" the emoji is : "+str(best_emoji)+" with distance of "+str(min_distance)+"!")
+        return best_emoji
+
+    #else:
+        #print("WRONG SENTIMENT VECTOR")
+
+
+# In[58]:
+
+
+def show_demo():
+    df_filtered = df[df["Unicode block"]=="Emoticons"]
+    all_smilies = list(df_filtered["Emoji"])
+
+    d_m = np.zeros(shape=(len(all_smilies),len(all_smilies)))
+
+    for c1 in range(len(all_smilies)):
+        for c2 in range(len(all_smilies)):
+            e1 = all_smilies[c1]
+            e2 = all_smilies[c2]
+
+            d = emoji_distance(e1,e2)
+            d_m[c1,c2] = d
+
+    for c in range(len(d_m[0])):
+        emoji = all_smilies[c]
+        row = d_m[c]
+        row_sorted = np.argsort(row)
+        #closest 5
+        r = row_sorted[0:10]
+        #print()
+        closest = ""
+        for i in r:
+            closest+=all_smilies[i]
+        print(emoji+": "+closest)
+
+
+# In[60]:
+
+
+#show_demo()
+
+
+# In[61]:
+
+
+#test bipolar matching entiment vector vs. emoji
+#df_filtered = df[df["Unicode block"]=="Emoticons"]
+#all_smilies = list(df_filtered["Emoji"])
+#for e in all_smilies:
+#    v2 = emoji_to_sentiment_vector(e)
+#    sentiment_vector_to_emoji(v2)