Release

TextMining.py

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+#TextMining Project: KeyWord Search
+#This script will search through a given paper or papers for the occurences of several keywords
+#it will then tell the user the most relevant paper based on the maximum number of occurences of a keyword or keywords
+#Claire Kincaid
+#April 18, 2016 (revised for MiniProject 5)
+
+import string
+
+def make_data(data):
+    """Takes a string, removes all punctuation, makes all letters lowercase and puts words of string into a list
+    >>> make_data("I'm hilarious")
+    ['im', 'hilarious']
+    """
+    data = data.lower() #turns all letters in string lowercase
+    data = data.translate(None, string.punctuation) #strips all punctuation from string
+    listdata = data.split()
+    return listdata
+
+def word_count(data):
+    """Takes a string, uses make_data to turn it into an analyzable list 
+    creates a dictionary that counts all words within that list
+    >>> word_count("I'm hilarious")
+    {'im': 1, 'hilarious': 1}
+    """
+    words = dict()
+    for word in make_data(data):
+        words[word] = words.get(word, 0) + 1
+    return words
+
+def word_find(data, keyword):
+    """ Takes a string, uses word_count to create dict counting all words in string
+    returns frequency of word specified as a keyword
+    >>> word_find("I'm hilarious", "hilarious")
+    1
+    """
+    hist = word_count(data)
+    return hist.get(keyword, 0)
+
+def multi_keywords_find(data, keywords):
+    """ Takes a string data and a list of keywords and returns dict w/ word count of those words
+    >>> multi_keywords_find("I'm hilarious", ['im', 'hilarious'])
+    {'im': 1, 'hilarious': 1}
+    """
+    all_keywords = dict()
+    for i in keywords:
+        all_keywords[i] = (word_find(data, i))
+    return all_keywords
+
+def multi_paper_word_find(data, keyword):
+    """takes string keyword, uses word_find to find the occurences of keyword in three datasets in a list
+    returns dictionary of papers in order of highest occurences of word to lowest"""
+    data_keyword = dict()
+    for i in data:
+        data_keyword[i] = word_find(i, keyword)
+    return data_keyword
+
+def relevance(data, keyword):
+    data_keyword = multi_paper_word_find(data, keyword)
+    most_relevant = max(data_keyword.get(data1, 0), data_keyword.get(data2, 0), data_keyword.get(data3, 0))
+    return most_relevant
+
+
+keywords = ['computer', 'assisted', 'collaborative', 'learning', 'inderdisciplinary', 'shared', 'knowledge']
+
+rawdata1 = open('data.txt')
+datalist1 = rawdata1.readlines()
+data1 = ''
+for i in datalist1:
+    data1 += i 
+
+rawdata2 = open('data2.txt')
+datalist2 = rawdata2.readlines()
+data2 = ''
+for i in datalist2:
+	data2 += i
+
+rawdata3 = open('data3.txt')
+datalist3 = rawdata3.readlines()
+data3 = ''
+for i in datalist3:
+	data3 += i
+
+data = [data1, data2, data3]
+
+print multi_keywords_find(data1, keywords)
+print relevance(data, keywords[1]
+
+import doctest
+doctest.testmod()