Naive approach mit word2Vec similarities

Project/naive_approach/naive_approach.py

@@ -15,6 +15,7 @@ from nltk.corpus import wordnet
 import math
 import pprint
 
+from gensim.models import Word2Vec, KeyedVectors
 
 # # Naive Approach
 table = pd.read_csv('../Tools/emoji_descriptions.csv')
@@ -29,25 +30,25 @@ for index, row in table.iterrows():
 # Helper functions
 #######################
 
-def stemming(messages):
-    stemmed_messages = []
+def stemming(message):
     ps = PorterStemmer()
-    for m in messages:
-        words = word_tokenize(m)
+    words = word_tokenize(message)
     sm = []
     for w in words:
         sm.append(ps.stem(w))
-        m = (" ").join(sm)
-        stemmed_messages.append(m)
-    return stemmed_messages
+    stemmed_message = (" ").join(sm)
+    return stemmed_message
 
 
 # * compare words to emoji descriptions
-def evaluate_sentence(sentence, description_key = 'description', lang = 'eng', emojis_to_consider="all"):
+def evaluate_sentence(sentence, description_key = 'description', lang = 'eng', emojis_to_consider="all", stem=True):
+    # assumes there is a trained w2v model stored in the same directory!
+    wv = KeyedVectors.load("word2vec.model", mmap='r')
     
+    if (stem):
+        sentence = stemming(sentence)
     tokenized_sentence = word_tokenize(sentence)
     n = len(tokenized_sentence)
-    l = table.shape[0]
     matrix_list = []
     
     for index in tableDict.keys():
@@ -57,20 +58,11 @@ def evaluate_sentence(sentence, description_key = 'description', lang = 'eng', e
         mat = np.zeros(shape=(m,n))
         for i in range(len(emoji_tokens)):
             for j in range(len(tokenized_sentence)):
-                syn1 = wordnet.synsets(emoji_tokens[i],lang=lang)
-                if len(syn1) == 0:
-                    continue
-                w1 = syn1[0]
-                #print(j, tokenized_sentence)
-                syn2 = wordnet.synsets(tokenized_sentence[j], lang=lang)
-                if len(syn2) == 0:
-                    continue
-                w2 = syn2[0]
-                val = w1.wup_similarity(w2)
-                if val is None:
+                try:
+                    val = wv.similarity(emoji_tokens[i], tokenized_sentence[j])
+                except KeyError:
                     continue
                 mat[i,j] = val
-        #print(row['character'], mat)
         matrix_list.append(mat)
             
     return matrix_list
@@ -83,10 +75,13 @@ def evaluate_sentence(sentence, description_key = 'description', lang = 'eng', e
 
 # load and preprocess data
 # emojis_to_consider can be either a list or "all"
-def prepareData(stemming=False):
-    if(stemming):
+def prepareData(stem=True, lower=True):
+    if(stem):
         for index in tableDict.keys():
             tableDict[index][1] = stemming(tableDict[index][1])
+    if(lower):
+        for index in tableDict.keys():
+            tableDict[index][1] = tableDict[index][1].lower()
     
     #collect the emojis
     lookup = {}