diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
@@ -1,3 +1,8 @@
 # TextMining
 
 This is the base repo for the text mining and analysis project for Software Design at Olin College.
+
+# commonwords.py
+Required packages:
+
+$ pip install wikipedia
diff --git a/Reflection.pdf b/Reflection.pdf
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diff --git a/commonwords.py b/commonwords.py
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+++ b/commonwords.py
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+import wikipedia
+import math
+
+def text_to_word(text):
+    """
+    Purpose: To refine the word being available to do further process.
+    The function replaces non-alphabet characters into space or blank as well as change all into lower character.
+    The string typed text is divided into multiple words by using split function.
+    """
+    text = text.replace('-',' ')
+    text = text.replace('(','')
+    text = text.replace(')','')
+    text = text.replace("'",'')
+    text = text.replace(".",'')
+    text = text.replace(",",'')
+    text = text.lower()
+    list_of_unsorted = text.split()
+    return list_of_unsorted
+
+def histogram(unsorted_words):
+    """
+    produces a dictionary composed of common word from two summary
+    in which the key and value is the word and the number of word.
+    """
+    count = dict()
+    for word in unsorted_words:
+        if word not in count:
+            count[word] = 1
+        else:
+            count[word] += 1
+    return count
+
+def matching_summary(search_1,search_2):
+    """
+    The function gets two arguments, which will be each summary of two words.
+    The arguments are refined by <text_to_word> function,
+    then it is used to construct the histogram of each word through <histogram> function.
+
+    For the common words which are both in each summary only can be stored inside of common_count dictionary, as a key.
+    The corresponding value is a geometric mean of the number of word in each dictionary from the arguments.
+    The specific words except preposition, pronoun, number, article, common verb, alphabet and conjunction
+    can be stored in the common_count dictionary.
+    """
+    print ('Common words in the summary of', search_1,'&', search_2)
+
+    text_1 = text_to_word(wikipedia.summary(search_1))
+    text_2 = text_to_word(wikipedia.summary(search_2))
+    histogram_1 = histogram(text_1)
+    histogram_2 = histogram(text_2)
+
+    preposition = ['as','by','in','at','on','with','of','for','to','through','after','from','over','until','during','under','all']
+    pronoun = ['i','me','my','he','his','him','she','her','hers','we','our','ours','it','its','they','them','their']
+    number = ['one','two','three','four','five','first','second','third']
+    article = ['also','an','a','the','non','most']
+    commonverb = ['is','are','was','were','be','been','have','has','had','get','got','gotten','can','could','make','made']
+    alphabet = ['a','b','c','d','e','g','f','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z','–','&']
+    conjunction = ['and','but','or','yet','that','which','where','what','when','why','how']
+
+    common_count = dict()
+    for word in text_1:
+        geometric_mean = math.sqrt(histogram_1.get(word, 0)*histogram_2.get(word, 0))
+        if geometric_mean != 0:
+            if word not in preposition and word not in pronoun and word not in article and word not in commonverb and word not in alphabet and word not in conjunction and word not in number:
+                common_count[word] = geometric_mean
+
+    return common_count
+
+import matplotlib.pyplot as plt
+
+word1 = input('1st word: ')
+word2 = input('2nd word: ')
+forxlabel = 'Common Words in Wikipedia Summary: ' + word1 + ' & ' + word2
+forylabel = 'Geometric Mean of Frequencey'
+
+"""
+Dictionary of common word gets changed into histogram with designated bar, sticks, and label of each axis.
+"""
+dictionary = matching_summary(word1,word2)
+fig = plt.figure()
+plt.bar(range(len(dictionary)), dictionary.values(), align='center')
+plt.xticks(range(len(dictionary)), dictionary.keys(), rotation=80)
+plt.xlabel(forxlabel)
+plt.ylabel(forylabel)
+plt.show()
+
+fig.savefig('image',dpi=600,bbox_inches='tight')
diff --git a/datafromwiki.py b/datafromwiki.py
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--- /dev/null
+++ b/datafromwiki.py
@@ -0,0 +1,42 @@
+import wikipedia
+import math
+
+def text_to_word(text):
+    text = text.replace('-',' ')
+    text = text.replace('(','')
+    text = text.replace(')','')
+    text = text.replace("'",'')
+    text = text.lower()
+    list_of_unsorted = text.split()
+    return list_of_unsorted
+
+def histogram(unsorted_words):
+    count = dict()
+    for word in unsorted_words:
+        if word not in count:
+            count[word] = 1
+        else:
+            count[word] += 1
+    return count
+
+#histogram(text_to_word('Subeen-is-(an)-idiot'))
+
+def matching_summary():
+    print ('Relationship between the given words will be presented')
+    search_1 = input('1st word: ')
+    search_2 = input('2nd word: ')
+    text_1 = text_to_word(wikipedia.summary(search_1))
+    text_2 = text_to_word(wikipedia.summary(search_2))
+    histogram_1 = histogram(text_1)
+    histogram_2 = histogram(text_2)
+
+    common_count = dict()
+    for word in text_1:
+        geometric_mean = math.sqrt(histogram_1.get(word, 0)*histogram_2.get(word, 0))
+        if geometric_mean != 0:
+            common_count[word] = geometric_mean
+        else:
+
+    return common_count
+
+matching_summary()