diff --git a/Project/naive_approach/naiveApproachTest.ipynb b/Project/naive_approach/naiveApproachTest.ipynb
new file mode 100644
index 0000000..104ad1f
--- /dev/null
+++ b/Project/naive_approach/naiveApproachTest.ipynb
@@ -0,0 +1,130 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import naive_approach"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "top_emojis = ['😂',\n",
+    "         '😭',\n",
+    "         '😍',\n",
+    "         '😩',\n",
+    "         '😊',\n",
+    "         '😘',\n",
+    "         '🙏',\n",
+    "         '🙌',\n",
+    "         '😉',\n",
+    "         '😁',\n",
+    "         '😅',\n",
+    "         '😎',\n",
+    "         '😢',\n",
+    "         '😒',\n",
+    "         '😏',\n",
+    "         '😌',\n",
+    "         '😔',\n",
+    "         '😋',\n",
+    "         '😀',\n",
+    "         '😤']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lookup = naive_approach.prepareData(emojis_to_consider=top_emojis)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sentence=\"I am very happy today\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "357",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-7-a7b8b0832a7d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnaive_approach\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msentence\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlookup\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0memojis_to_consider\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtop_emojis\u001b[0m\u001b[1;33m,\u001b[0m  \u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m~\\Desktop\\NLP-LAB\\Project\\naive_approach\\naive_approach.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(sentence, lookup, emojis_to_consider, criteria, description_key, lang, n, t)\u001b[0m\n\u001b[0;32m    117\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    118\u001b[0m     \u001b[1;31m# build a result table\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 119\u001b[1;33m     \u001b[0mtable_array\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlookup\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindexes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtable\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindexes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdescription_key\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    120\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    121\u001b[0m     \u001b[0mtable_frame\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtable_array\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcriteria\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'description'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Desktop\\NLP-LAB\\Project\\naive_approach\\naive_approach.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    117\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    118\u001b[0m     \u001b[1;31m# build a result table\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 119\u001b[1;33m     \u001b[0mtable_array\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlookup\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindexes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtable\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindexes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdescription_key\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    120\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    121\u001b[0m     \u001b[0mtable_frame\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtable_array\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcriteria\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'description'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mKeyError\u001b[0m: 357"
+     ]
+    }
+   ],
+   "source": [
+    "pred = naive_approach.predict(sentence, lookup, emojis_to_consider=top_emojis,  n=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['🎁', '🙋', '\\U0001f91f']"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Project/naive_approach/naive_approach.py b/Project/naive_approach/naive_approach.py
new file mode 100644
index 0000000..bf38f8a
--- /dev/null
+++ b/Project/naive_approach/naive_approach.py
@@ -0,0 +1,128 @@
+# coding: utf-8
+
+# In[1]:
+
+
+import pandas as pd
+from IPython.display import clear_output, Markdown, Math
+import ipywidgets as widgets
+import os
+import unicodedata as uni
+import numpy as np
+from nltk.stem import PorterStemmer
+from nltk.tokenize import sent_tokenize, word_tokenize
+from nltk.corpus import wordnet
+import math
+import pprint
+
+
+# # Naive Approach
+table = pd.read_csv('../Tools/emoji_descriptions.csv')
+
+#######################
+# Helper functions
+#######################
+
+def stemming(messages):
+    stemmed_messages = []
+    ps = PorterStemmer()
+    for m in messages:
+        words = word_tokenize(m)
+        sm = []
+        for w in words:
+            sm.append(ps.stem(w))
+        m = (" ").join(sm)
+        stemmed_messages.append(m)
+    return stemmed_messages
+
+
+# * compare words to emoji descriptions
+def evaluate_sentence(sentence, table, description_key = 'description', lang = 'eng'):
+    
+    tokenized_sentence = word_tokenize(sentence)
+    n = len(tokenized_sentence)
+    l = table.shape[0]
+    matrix_list = []
+    
+    for index, row in table.iterrows():
+        emoji_tokens = word_tokenize(row[description_key])
+        m = len(emoji_tokens)
+
+        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:
+                    continue
+                mat[i,j] = val
+        #print(row['character'], mat)
+        matrix_list.append(mat)
+            
+    return matrix_list
+    
+    
+###########################
+#Functions to be called from main script
+###########################
+    
+
+# load and preprocess data
+# emojis_to_consider can be either a list or "all"
+def prepareData(stemming=False, emojis_to_consider="all"):
+
+    table.head()
+    
+    if(stemming):
+        table['description'] = stemming(table['description'])
+    
+    #collect the emojis
+    lookup = {}
+    emoji_set = []
+    for index, row in table.iterrows():
+        if(emojis_to_consider=="all" or (type(emojis_to_consider)==list and row['character'] in emojis_to_consider)):
+            lookup[index] = row['character']
+            emoji_set.append(row['character'])
+
+    emoji_set = set(emoji_set)
+    
+    return lookup
+
+# make a prediction for an input sentence
+def predict(sentence, lookup, emojis_to_consider="all", criteria="threshold", description_key='description', lang = 'eng', n=10, t=0.9):
+
+    result = evaluate_sentence(sentence, table, description_key, lang)
+    
+    if(criteria=="summed"):
+        indexes = np.argsort([-np.sum(x) for x in result])[0:n]
+    elif (criteria=="max_val"):
+        indexes = np.argsort([-np.max(x) for x in result])[0:n]
+    elif(criteria=="avg"):
+        indexes = np.argsort([-np.mean(x) for x in result])[0:n]
+    else:
+        indexes= np.argsort([-len(np.where(x>t)[0]) / (x.shape[0] * x.shape[1]) for x in result])[0:n] 
+    
+    if(emojis_to_consider!="all"):
+        for i in indexes:
+            if (i not in lookup):
+                indexes = np.delete(indexes, [i])
+    
+    # build a result table
+    table_array = [[lookup[indexes[i]], str(table.iloc[indexes[i]][description_key])] for i in range(n) ]
+      
+    table_frame = pd.DataFrame(table_array, columns=[criteria, 'description'])
+    
+    #display(table_frame)
+    
+    return list(table_frame[criteria])
+
+#predict("I like to travel by train", description_key='description' , lang='eng')
+