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On implementing Taxonomy Creation under NLP domain, NLP (Natural Language Processing) is a combination of computer science and artificial intelligence [AI] concerned with, how to train computers to process and analyse large amounts of natural language data. NLP is the branch of machine learning and deep learning which is about analysing any text and giving  predictive analysis. Here in this problem statement, the given data is from Facebook which contains training and testing data sets, Train.csv contains 4 columns: Id, Title, Body, Tags. Test.csv contains the same columns but without Tags. From the given dataset we have to extract the keywords and to find the most used word.

Why: Problem statement

Business people sometimes face loss in their business. Lack of analysing the customer feedback may also lead to loss. The main problem is, they are not taking the customer reviews serious.

How: Solution description

In this project Taxonomy Creation, we have to extract the keywords and to find the most used word from the given dataset. Taxonomies are a useful way of categorizing information and extracting the keywords. It works on artificial intelligence, of deep learning algorithms. The purpose of deep learning is to train computers to execute tasks without human intervention. Deep learning approaches have acquired very high performance across many different Natural Language Processing [NLP] tasks. The major role of artificial intelligence is carried out by NLP using Natural Language Toolkit [NLTK] which is written in Python language. For categorizing the given information Tf-idf is used, Tf-idf stands for term frequency-inverse document frequency,

Tf-idf - is a numerical statistic that is conscious to reflect how important a word is to a document in a collection of documents. And hence by the above algorithm, tool, and vectorizer, we have solved this problem by extracting the keywords and also listed the most used keywords from Facebook data.


                                        [ Technical Flow Diagram  

Scope:

Here, we use NLP for analyzing a particular keyword mostly used in Facebook data using NLTK. For example, we have found that iPhone, sax, streaming, processing, XML, Linux, Javascript is the most often used keyword. After analysis, we found that iPhone, sax, streaming, processing, XML, Linux, Java script are the buzzwords on Facebook.

Out of Scope:

Here, we used a less number (10,000) of data. Initially, we considered 60 lakhs number of data. But it could not be performed in Windows. Even in Macbook, a more amount of time was taken to perform the task. So, we considered 10 thousand data for the study and we have executed the output.

Assumptions:

General Assumptions: In general, if a document is given, we will not be able to analyze a particular topic manually and efficiently.

Technical Assumptions: NLP can be used for technical analysis of a particular topic as it efficiently analyses the topic in a document.

Solution Approach:

As said, Taxonomies are a useful way of categorizing information. The given dataset contains raw data which has a numerical value, punctuation, special character, etc. First, we need to convert the raw data into meaningful data. Dividing the dataset into a training set and a test set is a good strategy, hence the datasets are split into train datasets and test datasets by using function train_test_split().

[ WORKING FLOW ]

By the method of Deep Learning, the NLP algorithm is implemented using NLTK in python language.

NLTK-  It contains text processing libraries for tokenization, stemming, Lemmatization, tagging.

Tokenization – it splits the text into tokens.

 

Stemming -  is a process of normalization, which reduces words to their root word. For example, connection, connected, connecting word reduce to a common word "connect".

Lemmatization – it reduces words to their base word, which is correct lemmas.

Tagging - Tags are strings that identify some property of each token.

In NLP, we use some library functions like Stop words, Word cloud, Word punct Tokenizer,Tf-idf vectorizer.

Stop words – is used to remove the unwanted words or not important words like (is, was, the, etc..,)

Word cloud – is used for viewing the stop words removed the phrase.

Word punct Tokenizer – removes the punctuations from the sentence and gives in the format of a series of words. 

Then, these series of words are converted into matrix format using the Coo_matrix function.

Matrix formatted words are vectorized using the Tf-idf vectorizer.

Tf-idf - is a numerical statistic that is conscious to reflect how important a word is to a document in a collection of documents.

By using tf-idf vectorizer, we have extracted the keyword, and also the frequency usage of those words in percentage.

 FREQUENCY OF THE WORDS:

                                                  

Initially, we extracted the keyword from the training dataset of all the four columns (Id, Title, Body, Tags), and then from the test dataset, finally from the extracted keywords of both train and test dataset, we again extract the most used keyword. 

The Tf-idf vectorizer shows the frequency usage of the words in percentage.

By using NLP for analyzing a particular keyword mostly used in Facebook data, we found that iPhone, sax, processing, XML, streaming, Linux, Javascript are the most often used keyword.  

SCREENSHOT OF THE OUTPUT:

EXTRACTED KEYWORD:

After analysis, we found that iPhone, sax, streaming, processing, XML, Linux, Java script are the buzzwords in Facebook.

 

Implementation Framework

  • Software: Anaconda tool

  • Language: Python 3.7

  • Working platform: Jupyter Notebook(Anaconda 3)

  • Domain: Artificial Intelligence [AI]

  • Method: Deep Learning

  • Algorithm: NLP algorithm

  • Toolkit: NLTK

  • Libraries used:  Stop words, Word cloud, Word punct Tokenizer, NumPy, Pandas, Tf-idf vectorizer

  • Technical Function: coo_matrix    

         After analysis, we found that iPhone, sax, streaming, processing, XML, Linux, Java script are the buzzwords in Facebook

 

How is it different from competition

We extracted around 10 keywords from the dataset which has 60 lakhs data. Minimum number of keywords leads to better understanding. Run time of the project is very fast when compared to others.

Who are your customers

Business magnate use this project to develop their companies.

Project Phases and Schedule

 

  • Data Collection

  • Data Cleaning 

  • Data analysis and visualization

  • NLP implementation

  • Keyword extraction

Resources Required

Software: Anaconda tool

Language: Python 3.7

Working platform: Jupyter Notebook(Anaconda 3)

Toolkit: NLTK

Libraries used:  Stop words, Word cloud, Word punct Tokenizer, NumPy, Pandas, Tf-idf vectorizer

Technical Function: coo_matrix    

Download:
Project Code Code copy
/* Your file Name : Taxonomy_creation (1).ipynb */
/* Your coding Language : python */
/* Your code snippet start here */
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import nltk as nlp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"small_train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Title</th>\n",
       "      <th>Body</th>\n",
       "      <th>Tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>How to check if an uploaded file is an image w...</td>\n",
       "      <td>&lt;p&gt;I'd like to check if an uploaded file is an...</td>\n",
       "      <td>php image-processing file-upload upload mime-t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>How can I prevent firefox from closing when I ...</td>\n",
       "      <td>&lt;p&gt;In my favorite editor (vim), I regularly us...</td>\n",
       "      <td>firefox</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>R Error Invalid type (list) for variable</td>\n",
       "      <td>&lt;p&gt;I am import matlab file and construct a dat...</td>\n",
       "      <td>r matlab machine-learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>How do I replace special characters in a URL?</td>\n",
       "      <td>&lt;p&gt;This is probably very simple, but I simply ...</td>\n",
       "      <td>c# url encoding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>How to modify whois contact details?</td>\n",
       "      <td>&lt;pre&gt;&lt;code&gt;function modify(.......)\\r\\n{\\r\\n  ...</td>\n",
       "      <td>php api file-get-contents</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id                                              Title  \\\n",
       "0   1  How to check if an uploaded file is an image w...   \n",
       "1   2  How can I prevent firefox from closing when I ...   \n",
       "2   3           R Error Invalid type (list) for variable   \n",
       "3   4      How do I replace special characters in a URL?   \n",
       "4   5               How to modify whois contact details?   \n",
       "\n",
       "                                                Body  \\\n",
       "0  <p>I'd like to check if an uploaded file is an...   \n",
       "1  <p>In my favorite editor (vim), I regularly us...   \n",
       "2  <p>I am import matlab file and construct a dat...   \n",
       "3  <p>This is probably very simple, but I simply ...   \n",
       "4  <pre><code>function modify(.......)\\r\\n{\\r\\n  ...   \n",
       "\n",
       "                                                Tags  \n",
       "0  php image-processing file-upload upload mime-t...  \n",
       "1                                            firefox  \n",
       "2                          r matlab machine-learning  \n",
       "3                                    c# url encoding  \n",
       "4                          php api file-get-contents  "
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "How can I prevent firefox from closing when I press ctrl-w ...\n",
      "\n",
      "total length 58\n"
     ]
    }
   ],
   "source": [
    "print(data.Title[1][:500], \"...\")\n",
    "print(\"\\ntotal length\", len(data.Title[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 4 columns):\n",
      "Id       10000 non-null int64\n",
      "Title    10000 non-null object\n",
      "Body     10000 non-null object\n",
      "Tags     10000 non-null object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 312.6+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "data.Title = data.Title.apply(lambda x: re.sub(\"(\\W)\", \" \", x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word_count</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13</td>\n",
       "      <td>How to check if an uploaded file is an image w...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12</td>\n",
       "      <td>How can I prevent firefox from closing when I ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>R Error Invalid type  list  for variable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>How do I replace special characters in a URL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>How to modify whois contact details</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   word_count                                              Title\n",
       "0          13  How to check if an uploaded file is an image w...\n",
       "1          12  How can I prevent firefox from closing when I ...\n",
       "2           7           R Error Invalid type  list  for variable\n",
       "3           9      How do I replace special characters in a URL \n",
       "4           6               How to modify whois contact details "
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer = nlp.WordPunctTokenizer()\n",
    "data[\"word_count\"] = data.Title.apply(lambda x: len(tokenizer.tokenize(x)))\n",
    "data[[\"word_count\", \"Title\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "to      3119\n",
      "in      2592\n",
      "a       2253\n",
      "How     1477\n",
      "the     1385\n",
      "of      1174\n",
      "with    1070\n",
      "and     1018\n",
      "for      867\n",
      "on       824\n",
      "dtype: int64\n",
      "intersection       1\n",
      "Feedback           1\n",
      "XSLX               1\n",
      "COCOS2D            1\n",
      "BarChart           1\n",
      "Visibility         1\n",
      "bapi_po_create1    1\n",
      "Returns            1\n",
      "Entire             1\n",
      "Fade               1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "freq = pd.Series(\" \".join(data.Title).split()).value_counts()\n",
    "print(freq.head(10))\n",
    "print(freq.tail(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package wordnet to /Users/learny/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('wordnet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "lemma = nlp.WordNetLemmatizer()\n",
    "data.Title = data.Title.apply(lambda x: lemma.lemmatize(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Title = data.Title.apply(lambda x: x.lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud, STOPWORDS\n",
    "from nltk.corpus import stopwords\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /Users/learny/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<wordcloud.wordcloud.WordCloud object at 0x1a2102e278>\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stopword_list = set(stopwords.words(\"english\"))\n",
    "\n",
    "word_cloud = WordCloud(\n",
    "                          background_color='black',\n",
    "                          stopwords=stopword_list,\n",
    "                          max_words=100,\n",
    "                          max_font_size=50, \n",
    "                          random_state=42\n",
    "                         ).generate(str(data.Title))\n",
    "print(word_cloud)\n",
    "fig = plt.figure(1)\n",
    "plt.imshow(word_cloud)\n",
    "plt.axis('off')\n",
    "plt.show()\n",
    "fig.savefig(\"word1.png\", dpi=900)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TfidfVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
       "        dtype=<class 'numpy.float64'>, encoding='utf-8', input='content',\n",
       "        lowercase=True, max_df=0.8, max_features=10000, min_df=1,\n",
       "        ngram_range=(1, 3), norm='l2', preprocessor=None, smooth_idf=True,\n",
       "        stop_words={\"doesn't\", 'herself', 'hadn', 'as', 'each', 'some', \"mustn't\", 'shouldn', 'his', 'off', 'most', 'not', 'few', 'isn', 'above', 'she', 'don', 'or', 'needn', 'o', \"haven't\", 'when', 'was', 'been', 'about', 'here', 'hasn', 'such', 'to', 'whom', \"won't\", 'will', 'theirs', 'who', 'while', 'onc...lf', 'in', 's', \"couldn't\", 'yourself', 'more', 'both', 'weren', 'i', 'we', 'why', 'doesn', \"she's\"},\n",
       "        strip_accents=None, sublinear_tf=False,\n",
       "        token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b', tokenizer=None, use_idf=True,\n",
       "        vocabulary=None)"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "tf_idf = TfidfVectorizer(max_df=0.8,stop_words=stopword_list, max_features=10000, ngram_range=(1,3))\n",
    "tf_idf.fit(data.Title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "doc = pd.Series(data.Title[500])\n",
    "doc_vector = tf_idf.transform(doc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function for sorting tf_idf in descending order\n",
    "from scipy.sparse import coo_matrix\n",
    "def sort_coo(coo_matrix):\n",
    "    tuples = zip(coo_matrix.col, coo_matrix.data)\n",
    "    return sorted(tuples, key=lambda x: (x[1], x[0]), reverse=True)\n",
    " \n",
    "def extract_topn_from_vector(feature_names, sorted_items, topn=10):\n",
    "    \"\"\"get the feature names and tf-idf score of top n items\"\"\"\n",
    "    \n",
    "    #use only topn items from vector\n",
    "    sorted_items = sorted_items[:topn]\n",
    "    score_vals = []\n",
    "    feature_vals = []\n",
    "    \n",
    "    # word index and corresponding tf-idf score\n",
    "    for idx, score in sorted_items:\n",
    "        \n",
    "        #keep track of feature name and its corresponding score\n",
    "        score_vals.append(round(score, 3))\n",
    "        feature_vals.append(feature_names[idx])\n",
    "\n",
    "    #create a tuples of feature,score\n",
    "    #results = zip(feature_vals,score_vals)\n",
    "    results= {}\n",
    "    for idx in range(len(feature_vals)):\n",
    "        results[feature_vals[idx]]=score_vals[idx]\n",
    "    \n",
    "    return results\n",
    "#sort the tf-idf vectors by descending order of scores\n",
    "sorted_items=sort_coo(doc_vector.tocoo())\n",
    "#extract only the top n; n here is 10\n",
    "feature_names = tf_idf.get_feature_names()\n",
    "keywords=extract_topn_from_vector(feature_names,sorted_items,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keywords in Title:\n",
      "streaming sax xml processing on iphone\n"
     ]
    }
   ],
   "source": [
    "# now print the results\n",
    "print(\"Keywords in Title:\")\n",
    "print(doc[0][:10000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keywords in Title:\n",
      "processing iphone 0.513\n",
      "sax 0.476\n",
      "streaming 0.421\n",
      "processing 0.398\n",
      "iphone 0.3\n"
     ]
    }
   ],
   "source": [
    "print(\"Keywords in Title:\")\n",
    "for k in keywords:\n",
    "    print(k,keywords[k])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<p>In my favorite editor (vim), I regularly use ctrl-w to execute a certain action. Now, it quite often happens to me that firefox is the active window (on windows) while I still look at vim (thinking vim is the active window) and press ctrl-w which closes firefox. This is not what I want. Is there a way to stop ctrl-w from closing firefox?</p>\r\n",
      "\r\n",
      "<p>Rene</p>\r\n",
      " ...\n",
      "\n",
      "total length 363\n"
     ]
    }
   ],
   "source": [
    "print(data.Body[1][:500], \"...\")\n",
    "print(\"\\ntotal length\", len(data.Body[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Body = data.Body.apply(lambda x: re.sub(\"(\\W)\", \" \", x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word_count</th>\n",
       "      <th>Body</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>83</td>\n",
       "      <td>p I d like to check if an uploaded file is an...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>71</td>\n",
       "      <td>p In my favorite editor  vim   I regularly us...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3161</td>\n",
       "      <td>p I am import matlab file and construct a dat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>115</td>\n",
       "      <td>p This is probably very simple  but I simply ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>148</td>\n",
       "      <td>pre  code function modify                 mco...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   word_count                                               Body\n",
       "0          83   p I d like to check if an uploaded file is an...\n",
       "1          71   p In my favorite editor  vim   I regularly us...\n",
       "2        3161   p I am import matlab file and construct a dat...\n",
       "3         115   p This is probably very simple  but I simply ...\n",
       "4         148   pre  code function modify                 mco..."
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer = nlp.WordPunctTokenizer()\n",
    "data[\"word_count\"] = data.Body.apply(lambda x: len(tokenizer.tokenize(x)))\n",
    "data[[\"word_count\", \"Body\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p       82119\n",
      "the     48956\n",
      "I       39324\n",
      "to      35505\n",
      "code    34428\n",
      "gt      31392\n",
      "a       31339\n",
      "lt      27851\n",
      "is      18499\n",
      "pre     18300\n",
      "dtype: int64\n",
      "filetypes                 1\n",
      "xdmcp                     1\n",
      "C01137_1                  1\n",
      "Slackware                 1\n",
      "openSUSE                  1\n",
      "ServiceControllerProxy    1\n",
      "yrange                    1\n",
      "Employer                  1\n",
      "sethostname               1\n",
      "suitably                  1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "freq = pd.Series(\" \".join(data.Body).split()).value_counts()\n",
    "print(freq.head(10))\n",
    "print(freq.tail(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "lemma = nlp.WordNetLemmatizer()\n",
    "data.Body = data.Body.apply(lambda x: lemma.lemmatize(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Body = data.Body.apply(lambda x: x.lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<wordcloud.wordcloud.WordCloud object at 0x1a3931d4e0>\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stopword_list = set(stopwords.words(\"english\"))\n",
    "\n",
    "word_cloud = WordCloud(\n",
    "                          background_color='black',\n",
    "                          stopwords=stopword_list,\n",
    "                          max_words=100,\n",
    "                          max_font_size=50, \n",
    "                          random_state=42\n",
    "                         ).generate(str(data.Body))\n",
    "print(word_cloud)\n",
    "fig = plt.figure(1)\n",
    "plt.imshow(word_cloud)\n",
    "plt.axis('off')\n",
    "plt.show()\n",
    "fig.savefig(\"word2.png\", dpi=900)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TfidfVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
       "        dtype=<class 'numpy.float64'>, encoding='utf-8', input='content',\n",
       "        lowercase=True, max_df=0.8, max_features=10000, min_df=1,\n",
       "        ngram_range=(1, 3), norm='l2', preprocessor=None, smooth_idf=True,\n",
       "        stop_words={\"doesn't\", 'herself', 'hadn', 'as', 'each', 'some', \"mustn't\", 'shouldn', 'his', 'off', 'most', 'not', 'few', 'isn', 'above', 'she', 'don', 'or', 'needn', 'o', \"haven't\", 'when', 'was', 'been', 'about', 'here', 'hasn', 'such', 'to', 'whom', \"won't\", 'will', 'theirs', 'who', 'while', 'onc...lf', 'in', 's', \"couldn't\", 'yourself', 'more', 'both', 'weren', 'i', 'we', 'why', 'doesn', \"she's\"},\n",
       "        strip_accents=None, sublinear_tf=False,\n",
       "        token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b', tokenizer=None, use_idf=True,\n",
       "        vocabulary=None)"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "tf_idf = TfidfVectorizer(max_df=0.8,stop_words=stopword_list, max_features=10000, ngram_range=(1,3))\n",
    "tf_idf.fit(data.Body)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "doc = pd.Series(data.Body[500])\n",
    "doc_vector = tf_idf.transform(doc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function for sorting tf_idf in descending order\n",
    "from scipy.sparse import coo_matrix\n",
    "def sort_coo(coo_matrix):\n",
    "    tuples = zip(coo_matrix.col, coo_matrix.data)\n",
    "    return sorted(tuples, key=lambda x: (x[1], x[0]), reverse=True)\n",
    " \n",
    "def extract_topn_from_vector(feature_names, sorted_items, topn=10):\n",
    "    \"\"\"get the feature names and tf-idf score of top n items\"\"\"\n",
    "    \n",
    "    #use only topn items from vector\n",
    "    sorted_items = sorted_items[:topn]\n",
    "    score_vals = []\n",
    "    feature_vals = []\n",
    "    \n",
    "    # word index and corresponding tf-idf score\n",
    "    for idx, score in sorted_items:\n",
    "        \n",
    "        #keep track of feature name and its corresponding score\n",
    "        score_vals.append(round(score, 3))\n",
    "        feature_vals.append(feature_names[idx])\n",
    "\n",
    "    #create a tuples of feature,score\n",
    "    #results = zip(feature_vals,score_vals)\n",
    "    results= {}\n",
    "    for idx in range(len(feature_vals)):\n",
    "        results[feature_vals[idx]]=score_vals[idx]\n",
    "    \n",
    "    return results\n",
    "#sort the tf-idf vectors by descending order of scores\n",
    "sorted_items=sort_coo(doc_vector.tocoo())\n",
    "#extract only the top n; n here is 10\n",
    "feature_names = tf_idf.get_feature_names()\n",
    "keywords1=extract_topn_from_vector(feature_names,sorted_items,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keywords in Body:\n",
      " p nsxmlparser allows sax parsing of either an nsdata block or from a url source   p      p the problem is that both these methods require the entire xml source to be known before parsing begins   p      p suppose i have a stream of xml data  say a sequence of nsdata objects  and i want to process the stream using nsxmlparser or another cocoa class  how can i do this without needing to have the whole document to begin with   p   \n"
     ]
    }
   ],
   "source": [
    "print(\"Keywords in Body:\")\n",
    "print(doc[0][:10000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keywords in Body:\n",
      "nsdata 0.419\n",
      "parsing 0.359\n",
      "stream 0.323\n",
      "source 0.241\n",
      "xml 0.24\n"
     ]
    }
   ],
   "source": [
    "print(\"Keywords in Body:\")\n",
    "for m in keywords1:\n",
    "    print(m,keywords1[m])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "firefox ...\n",
      "\n",
      "total length 7\n"
     ]
    }
   ],
   "source": [
    "print(data.Tags[1][:500], \"...\")\n",
    "print(\"\\ntotal length\", len(data.Tags[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Tags = data.Tags.apply(lambda x: re.sub(\"(\\W)\", \" \", x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c             1565\n",
      "net            960\n",
      "java           753\n",
      "php            726\n",
      "android        699\n",
      "jquery         654\n",
      "javascript     651\n",
      "asp            544\n",
      "sql            534\n",
      "windows        477\n",
      "dtype: int64\n",
      "revert           1\n",
      "effect           1\n",
      "cin              1\n",
      "commandbutton    1\n",
      "vexing           1\n",
      "cars             1\n",
      "xstring          1\n",
      "glut             1\n",
      "nicedit          1\n",
      "backups          1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "freq = pd.Series(\" \".join(data.Tags).split()).value_counts()\n",
    "print(freq.head(10))\n",
    "print(freq.tail(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "lemma = nlp.WordNetLemmatizer()\n",
    "data.Tags = data.Tags.apply(lambda x: lemma.lemmatize(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Tags = data.Tags.apply(lambda x: x.lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<wordcloud.wordcloud.WordCloud object at 0x1a2b6551d0>\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stopword_list = set(stopwords.words(\"english\"))\n",
    "\n",
    "word_cloud = WordCloud(\n",
    "                          background_color='black',\n",
    "                          stopwords=stopword_list,\n",
    "                          max_words=100,\n",
    "                          max_font_size=50, \n",
    "                          random_state=42\n",
    "                         ).generate(str(data.Tags))\n",
    "print(word_cloud)\n",
    "fig = plt.figure(1)\n",
    "plt.imshow(word_cloud)\n",
    "plt.axis('off')\n",
    "plt.show()\n",
    "fig.savefig(\"word3.png\", dpi=900)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TfidfVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
       "        dtype=<class 'numpy.float64'>, encoding='utf-8', input='content',\n",
       "        lowercase=True, max_df=0.8, max_features=10000, min_df=1,\n",
       "        ngram_range=(1, 3), norm='l2', preprocessor=None, smooth_idf=True,\n",
       "        stop_words={\"doesn't\", 'herself', 'hadn', 'as', 'each', 'some', \"mustn't\", 'shouldn', 'his', 'off', 'most', 'not', 'few', 'isn', 'above', 'she', 'don', 'or', 'needn', 'o', \"haven't\", 'when', 'was', 'been', 'about', 'here', 'hasn', 'such', 'to', 'whom', \"won't\", 'will', 'theirs', 'who', 'while', 'onc...lf', 'in', 's', \"couldn't\", 'yourself', 'more', 'both', 'weren', 'i', 'we', 'why', 'doesn', \"she's\"},\n",
       "        strip_accents=None, sublinear_tf=False,\n",
       "        token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b', tokenizer=None, use_idf=True,\n",
       "        vocabulary=None)"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "tf_idf = TfidfVectorizer(max_df=0.8,stop_words=stopword_list, max_features=10000, ngram_range=(1,3))\n",
    "tf_idf.fit(data.Tags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "doc = pd.Series(data.Tags[500])\n",
    "doc_vector = tf_idf.transform(doc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function for sorting tf_idf in descending order\n",
    "from scipy.sparse import coo_matrix\n",
    "def sort_coo(coo_matrix):\n",
    "    tuples = zip(coo_matrix.col, coo_matrix.data)\n",
    "    return sorted(tuples, key=lambda x: (x[1], x[0]), reverse=True)\n",
    " \n",
    "def extract_topn_from_vector(feature_names, sorted_items, topn=10):\n",
    "    \"\"\"get the feature names and tf-idf score of top n items\"\"\"\n",
    "    \n",
    "    #use only topn items from vector\n",
    "    sorted_items = sorted_items[:topn]\n",
    "    score_vals = []\n",
    "    feature_vals = []\n",
    "    \n",
    "    # word index and corresponding tf-idf score\n",
    "    for idx, score in sorted_items:\n",
    "        \n",
    "        #keep track of feature name and its corresponding score\n",
    "        score_vals.append(round(score, 3))\n",
    "        feature_vals.append(feature_names[idx])\n",
    "\n",
    "    #create a tuples of feature,score\n",
    "    #results = zip(feature_vals,score_vals)\n",
    "    results= {}\n",
    "    for idx in range(len(feature_vals)):\n",
    "        results[feature_vals[idx]]=score_vals[idx]\n",
    "    \n",
    "    return results\n",
    "#sort the tf-idf vectors by descending order of scores\n",
    "sorted_items=sort_coo(doc_vector.tocoo())\n",
    "#extract only the top n; n here is 10\n",
    "feature_names = tf_idf.get_feature_names()\n",
    "keywords2=extract_topn_from_vector(feature_names,sorted_items,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keywords in Tags:\n",
      "iphone xml cocoa streaming sax\n"
     ]
    }
   ],
   "source": [
    "print(\"Keywords in Tags:\")\n",
    "print(doc[0][:10000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keywords in Tags:\n",
      "xml cocoa 0.427\n",
      "sax 0.427\n",
      "iphone xml cocoa 0.427\n",
      "iphone xml 0.403\n",
      "streaming 0.338\n"
     ]
    }
   ],
   "source": [
    "print(\"Keywords in Tags:\")\n",
    "for n in keywords2:\n",
    "    print(n,keywords2[n])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total keywords in the whole training data\n",
      "processing iphone 0.513\n",
      "sax 0.476\n",
      "streaming 0.421\n",
      "processing 0.398\n",
      "iphone 0.3\n",
      "nsdata 0.419\n",
      "parsing 0.359\n",
      "stream 0.323\n",
      "source 0.241\n",
      "xml 0.24\n",
      "xml cocoa 0.427\n",
      "sax 0.427\n",
      "iphone xml cocoa 0.427\n",
      "iphone xml 0.403\n",
      "streaming 0.338\n"
     ]
    }
   ],
   "source": [
    "print(\"Total keywords in the whole training data\")\n",
    "for k in keywords:\n",
    "    print(k,keywords[k])\n",
    "for m in keywords1:\n",
    "    print(m,keywords1[m])\n",
    "for n in keywords2:\n",
    "    print(n,keywords2[n])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = pd.read_csv(\"small_test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Title</th>\n",
       "      <th>Body</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6034196</td>\n",
       "      <td>Getting rid of site-specific hotkeys</td>\n",
       "      <td>&lt;p&gt;How do I disable site-specific hotkeys if (...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6034197</td>\n",
       "      <td>Nodes inside Cisco VPN. Incoming SSH requests ...</td>\n",
       "      <td>&lt;p&gt;I've a gateway-to-gateway VPN setup between...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6034198</td>\n",
       "      <td>Remove old vCenter servers from VMWare vSphere...</td>\n",
       "      <td>&lt;p&gt;After changing our vCenter servers recently...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6034199</td>\n",
       "      <td>Replace &lt;span&gt; element with var containing html</td>\n",
       "      <td>&lt;p&gt;I have a variable i lifted the contents of,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6034200</td>\n",
       "      <td>Will PHP included html content affect my seo?</td>\n",
       "      <td>&lt;p&gt;Today i purchase a small CMS system. With t...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id                                              Title  \\\n",
       "0  6034196               Getting rid of site-specific hotkeys   \n",
       "1  6034197  Nodes inside Cisco VPN. Incoming SSH requests ...   \n",
       "2  6034198  Remove old vCenter servers from VMWare vSphere...   \n",
       "3  6034199    Replace <span> element with var containing html   \n",
       "4  6034200      Will PHP included html content affect my seo?   \n",
       "\n",
       "                                                Body  \n",
       "0  <p>How do I disable site-specific hotkeys if (...  \n",
       "1  <p>I've a gateway-to-gateway VPN setup between...  \n",
       "2  <p>After changing our vCenter servers recently...  \n",
       "3  <p>I have a variable i lifted the contents of,...  \n",
       "4  <p>Today i purchase a small CMS system. With t...  "
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nodes inside Cisco VPN. Incoming SSH requests allowed. But can't initiate an outbound SSH ...\n",
      "\n",
      "total length 89\n"
     ]
    }
   ],
   "source": [
    "print(test_data.Title[1][:500], \"...\")\n",
    "print(\"\\ntotal length\", len(test_data.Title[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3000 entries, 0 to 2999\n",
      "Data columns (total 3 columns):\n",
      "Id       3000 non-null int64\n",
      "Title    3000 non-null object\n",
      "Body     3000 non-null object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 70.4+ KB\n"
     ]
    }
   ],
   "source": [
    "test_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data.Title = test_data.Title.apply(lambda x: re.sub(\"(\\W)\", \" \", x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word_count</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>Getting rid of site specific hotkeys</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>Nodes inside Cisco VPN  Incoming SSH requests ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>Remove old vCenter servers from VMWare vSphere...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>Replace  span  element with var containing html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>Will PHP included html content affect my seo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   word_count                                              Title\n",
       "0           6               Getting rid of site specific hotkeys\n",
       "1          15  Nodes inside Cisco VPN  Incoming SSH requests ...\n",
       "2          10  Remove old vCenter servers from VMWare vSphere...\n",
       "3           7    Replace  span  element with var containing html\n",
       "4           8      Will PHP included html content affect my seo "
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer = nlp.WordPunctTokenizer()\n",
    "test_data[\"word_count\"] = test_data.Title.apply(lambda x: len(tokenizer.tokenize(x)))\n",
    "test_data[[\"word_count\", \"Title\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "to      929\n",
      "a       775\n",
      "in      760\n",
      "the     426\n",
      "How     425\n",
      "of      373\n",
      "with    328\n",
      "and     326\n",
      "on      277\n",
      "for     245\n",
      "dtype: int64\n",
      "offline         1\n",
      "digital         1\n",
      "GRUB            1\n",
      "dbproj          1\n",
      "Machine         1\n",
      "Unstructured    1\n",
      "CoreData        1\n",
      "invite          1\n",
      "contend         1\n",
      "Keyboard        1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "freq = pd.Series(\" \".join(test_data.Title).split()).value_counts()\n",
    "print(freq.head(10))\n",
    "print(freq.tail(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "lemma = nlp.WordNetLemmatizer()\n",
    "test_data.Title = test_data.Title.apply(lambda x: lemma.lemmatize(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data.Title = test_data.Title.apply(lambda x: x.lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<wordcloud.wordcloud.WordCloud object at 0x1a2b20f080>\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stopword_list = set(stopwords.words(\"english\"))\n",
    "\n",
    "word_cloud = WordCloud(\n",
    "                          background_color='black',\n",
    "                          stopwords=stopword_list,\n",
    "                          max_words=100,\n",
    "                          max_font_size=50, \n",
    "                          random_state=42\n",
    "                         ).generate(str(test_data.Title))\n",
    "print(word_cloud)\n",
    "fig = plt.figure(1)\n",
    "plt.imshow(word_cloud)\n",
    "plt.axis('off')\n",
    "plt.show()\n",
    "fig.savefig(\"test_word1.png\", dpi=900)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TfidfVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
       "        dtype=<class 'numpy.float64'>, encoding='utf-8', input='content',\n",
       "        lowercase=True, max_df=0.8, max_features=10000, min_df=1,\n",
       "        ngram_range=(1, 3), norm='l2', preprocessor=None, smooth_idf=True,\n",
       "        stop_words={\"doesn't\", 'herself', 'hadn', 'as', 'each', 'some', \"mustn't\", 'shouldn', 'his', 'off', 'most', 'not', 'few', 'isn', 'above', 'she', 'don', 'or', 'needn', 'o', \"haven't\", 'when', 'was', 'been', 'about', 'here', 'hasn', 'such', 'to', 'whom', \"won't\", 'will', 'theirs', 'who', 'while', 'onc...lf', 'in', 's', \"couldn't\", 'yourself', 'more', 'both', 'weren', 'i', 'we', 'why', 'doesn', \"she's\"},\n",
       "        strip_accents=None, sublinear_tf=False,\n",
       "        token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b', tokenizer=None, use_idf=True,\n",
       "        vocabulary=None)"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "tf_idf = TfidfVectorizer(max_df=0.8,stop_words=stopword_list, max_features=10000, ngram_range=(1,3))\n",
    "tf_idf.fit(test_data.Title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "doc = pd.Series(test_data.Title[500])\n",
    "doc_vector = tf_idf.transform(doc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function for sorting tf_idf in descending order\n",
    "from scipy.sparse import coo_matrix\n",
    "def sort_coo(coo_matrix):\n",
    "    tuples = zip(coo_matrix.col, coo_matrix.data)\n",
    "    return sorted(tuples, key=lambda x: (x[1], x[0]), reverse=True)\n",
    " \n",
    "def extract_topn_from_vector(feature_names, sorted_items, topn=10):\n",
    "    \"\"\"get the feature names and tf-idf score of top n items\"\"\"\n",
    "    \n",
    "    #use only topn items from vector\n",
    "    sorted_items = sorted_items[:topn]\n",
    "    score_vals = []\n",
    "    feature_vals = []\n",
    "    \n",
    "    # word index and corresponding tf-idf score\n",
    "    for idx, score in sorted_items:\n",
    "        \n",
    "        #keep track of feature name and its corresponding score\n",
    "        score_vals.append(round(score, 3))\n",
    "        feature_vals.append(feature_names[idx])\n",
    "\n",
    "    #create a tuples of feature,score\n",
    "    #results = zip(feature_vals,score_vals)\n",
    "    results= {}\n",
    "    for idx in range(len(feature_vals)):\n",
    "        results[feature_vals[idx]]=score_vals[idx]\n",
    "    \n",
    "    return results\n",
    "#sort the tf-idf vectors by descending order of scores\n",
    "sorted_items=sort_coo(doc_vector.tocoo())\n",
    "#extract only the top n; n here is 10\n",
    "feature_names = tf_idf.get_feature_names()\n",
    "test_keywords=extract_topn_from_vector(feature_names,sorted_items,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keywords in Title - test:\n",
      "hard symlinks and alternate data streams\n"
     ]
    }
   ],
   "source": [
    "print(\"Keywords in Title - test:\")\n",
    "print(doc[0][:10000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keywords in Title - Test:\n",
      "streams 0.588\n",
      "alternate 0.559\n",
      "hard 0.482\n",
      "data 0.331\n"
     ]
    }
   ],
   "source": [
    "print(\"Keywords in Title - Test:\")\n",
    "for l in test_keywords:\n",
    "    print(l,test_keywords[l])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<p>I've a gateway-to-gateway VPN setup between my Linksys RV042 router and a Cisco VPN. </p>\r\n",
      "\r\n",
      "<p>I am able to SSH into any of the machine inside the VPN from my network. But none of the machines inside the VPN can initiate an SSH into my network. It seems they've blocked even all ping requests to my network gateway.</p>\r\n",
      "\r\n",
      "<p>This is the requirement: I have scripts that SSH into the machines inside the VPN and run a long mysql query. The query generates an output to a file. The time that these ...\n",
      "\n",
      "total length 903\n"
     ]
    }
   ],
   "source": [
    "print(test_data.Body[1][:500], \"...\")\n",
    "print(\"\\ntotal length\", len(test_data.Body[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data.Body = test_data.Body.apply(lambda x: re.sub(\"(\\W)\", \" \", x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word_count</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>107</td>\n",
       "      <td>getting rid of site specific hotkeys</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>173</td>\n",
       "      <td>nodes inside cisco vpn  incoming ssh requests ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42</td>\n",
       "      <td>remove old vcenter servers from vmware vsphere...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>101</td>\n",
       "      <td>replace  span  element with var containing html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>166</td>\n",
       "      <td>will php included html content affect my seo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   word_count                                              Title\n",
       "0         107               getting rid of site specific hotkeys\n",
       "1         173  nodes inside cisco vpn  incoming ssh requests ...\n",
       "2          42  remove old vcenter servers from vmware vsphere...\n",
       "3         101    replace  span  element with var containing html\n",
       "4         166      will php included html content affect my seo "
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer = nlp.WordPunctTokenizer()\n",
    "test_data[\"word_count\"] = test_data.Body.apply(lambda x: len(tokenizer.tokenize(x)))\n",
    "test_data[[\"word_count\", \"Title\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p       24247\n",
      "the     14730\n",
      "I       11759\n",
      "to      10569\n",
      "code    10161\n",
      "a        9760\n",
      "gt       9189\n",
      "lt       8288\n",
      "is       5562\n",
      "and      5488\n",
      "dtype: int64\n",
      "hasChildNodes     1\n",
      "Strongly          1\n",
      "153               1\n",
      "eOpts             1\n",
      "864               1\n",
      "406               1\n",
      "invoice_number    1\n",
      "otro              1\n",
      "__newindex        1\n",
      "revisions         1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "freq = pd.Series(\" \".join(test_data.Body).split()).value_counts()\n",
    "print(freq.head(10))\n",
    "print(freq.tail(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "lemma = nlp.WordNetLemmatizer()\n",
    "test_data.Body = test_data.Body.apply(lambda x: lemma.lemmatize(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data.Body = test_data.Body.apply(lambda x: x.lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<wordcloud.wordcloud.WordCloud object at 0x1a2b5e4080>\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stopword_list = set(stopwords.words(\"english\"))\n",
    "\n",
    "word_cloud = WordCloud(\n",
    "                          background_color='black',\n",
    "                          stopwords=stopword_list,\n",
    "                          max_words=100,\n",
    "                          max_font_size=50, \n",
    "                          random_state=42\n",
    "                         ).generate(str(test_data.Body))\n",
    "print(word_cloud)\n",
    "fig = plt.figure(1)\n",
    "plt.imshow(word_cloud)\n",
    "plt.axis('off')\n",
    "plt.show()\n",
    "fig.savefig(\"test_word2.png\", dpi=900)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TfidfVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
       "        dtype=<class 'numpy.float64'>, encoding='utf-8', input='content',\n",
       "        lowercase=True, max_df=0.8, max_features=10000, min_df=1,\n",
       "        ngram_range=(1, 3), norm='l2', preprocessor=None, smooth_idf=True,\n",
       "        stop_words={\"doesn't\", 'herself', 'hadn', 'as', 'each', 'some', \"mustn't\", 'shouldn', 'his', 'off', 'most', 'not', 'few', 'isn', 'above', 'she', 'don', 'or', 'needn', 'o', \"haven't\", 'when', 'was', 'been', 'about', 'here', 'hasn', 'such', 'to', 'whom', \"won't\", 'will', 'theirs', 'who', 'while', 'onc...lf', 'in', 's', \"couldn't\", 'yourself', 'more', 'both', 'weren', 'i', 'we', 'why', 'doesn', \"she's\"},\n",
       "        strip_accents=None, sublinear_tf=False,\n",
       "        token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b', tokenizer=None, use_idf=True,\n",
       "        vocabulary=None)"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "tf_idf = TfidfVectorizer(max_df=0.8,stop_words=stopword_list, max_features=10000, ngram_range=(1,3))\n",
    "tf_idf.fit(test_data.Body)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "doc = pd.Series(test_data.Body[500])\n",
    "doc_vector = tf_idf.transform(doc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function for sorting tf_idf in descending order\n",
    "from scipy.sparse import coo_matrix\n",
    "def sort_coo(coo_matrix):\n",
    "    tuples = zip(coo_matrix.col, coo_matrix.data)\n",
    "    return sorted(tuples, key=lambda x: (x[1], x[0]), reverse=True)\n",
    " \n",
    "def extract_topn_from_vector(feature_names, sorted_items, topn=10):\n",
    "    \"\"\"get the feature names and tf-idf score of top n items\"\"\"\n",
    "    \n",
    "    #use only topn items from vector\n",
    "    sorted_items = sorted_items[:topn]\n",
    "    score_vals = []\n",
    "    feature_vals = []\n",
    "    \n",
    "    # word index and corresponding tf-idf score\n",
    "    for idx, score in sorted_items:\n",
    "        \n",
    "        #keep track of feature name and its corresponding score\n",
    "        score_vals.append(round(score, 3))\n",
    "        feature_vals.append(feature_names[idx])\n",
    "\n",
    "    #create a tuples of feature,score\n",
    "    #results = zip(feature_vals,score_vals)\n",
    "    results= {}\n",
    "    for idx in range(len(feature_vals)):\n",
    "        results[feature_vals[idx]]=score_vals[idx]\n",
    "    \n",
    "    return results\n",
    "#sort the tf-idf vectors by descending order of scores\n",
    "sorted_items=sort_coo(doc_vector.tocoo())\n",
    "#extract only the top n; n here is 10\n",
    "feature_names = tf_idf.get_feature_names()\n",
    "test_keywords1=extract_topn_from_vector(feature_names,sorted_items,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keywords in Body - test:\n",
      " p an interesting thought just occurred to me while thinking about ntfs    p      p ntfs supports hard links  symbolic links  and alternate data streams  is it possible for an ads to be a link to another file  conversely  do the alternate data streams attached to a link belong to the link itself or to the underlying filesystem data   p   \n"
     ]
    }
   ],
   "source": [
    "print(\"Keywords in Body - test:\")\n",
    "print(doc[0][:10000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keywords in Body - Test:\n",
      "streams 0.588\n",
      "alternate 0.559\n",
      "hard 0.482\n",
      "data 0.331\n"
     ]
    }
   ],
   "source": [
    "print(\"Keywords in Body - Test:\")\n",
    "for s in test_keywords1:\n",
    "    print(s,test_keywords1[s])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total keywords in the whole training data\n",
      "processing iphone 0.513\n",
      "sax 0.476\n",
      "streaming 0.421\n",
      "processing 0.398\n",
      "iphone 0.3\n",
      "nsdata 0.419\n",
      "parsing 0.359\n",
      "stream 0.323\n",
      "source 0.241\n",
      "xml 0.24\n",
      "xml cocoa 0.427\n",
      "sax 0.427\n",
      "iphone xml cocoa 0.427\n",
      "iphone xml 0.403\n",
      "streaming 0.338\n"
     ]
    }
   ],
   "source": [
    "print(\"Total keywords in the whole training data\")\n",
    "for k in keywords:\n",
    "    print(k,keywords[k])\n",
    "for m in keywords1:\n",
    "    print(m,keywords1[m])\n",
    "for n in keywords2:\n",
    "    print(n,keywords2[n])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total keywords in the whole testing data\n",
      "streams 0.588\n",
      "alternate 0.559\n",
      "hard 0.482\n",
      "data 0.331\n",
      "ntfs 0.435\n",
      "alternate 0.425\n",
      "link 0.354\n",
      "links 0.296\n",
      "data 0.265\n"
     ]
    }
   ],
   "source": [
    "print(\"Total keywords in the whole testing data\")\n",
    "for l in test_keywords:\n",
    "    print(l,test_keywords[l])\n",
    "for s in test_keywords1:\n",
    "    print(s,test_keywords1[s])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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Project period

06/30/2019 - 10/15/2019

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