POS_Text_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5af55eb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports:\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter\n",
    "\n",
    "import nltk\n",
    "from nltk.corpus import stopwords, wordnet\n",
    "from nltk import pos_tag, ne_chunk\n",
    "from nltk.stem import WordNetLemmatizer, PorterStemmer\n",
    "from nltk.tokenize import word_tokenize, sent_tokenize\n",
    "\n",
    "import spacy\n",
    "# Load Spacy's language model:\n",
    "nlp = spacy.load('en_core_web_lg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99bb742f",
   "metadata": {},
   "source": [
    "### Preparing the Data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6e429afa",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>ID</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26-Jul-17</td>\n",
       "      <td>1.010040e+11</td>\n",
       "      <td>please call daughter Dianne Thomas 07920 07565...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28-Jul-17</td>\n",
       "      <td>1.010040e+11</td>\n",
       "      <td>please speak to sister - Alyson Powell on abpv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28-Jul-17</td>\n",
       "      <td>1.010040e+11</td>\n",
       "      <td>KELLY HAS RUNG - HER PARENTS ARE DUE TO HAVE W...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31-Jul-17</td>\n",
       "      <td>1.010040e+11</td>\n",
       "      <td>Mark Hitchings, Scheme Manager Swn Yr Afon con...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28-Jul-17</td>\n",
       "      <td>1.010040e+11</td>\n",
       "      <td>SON BRIAN HASFORD RUN G- HE IS RESIDENT IN AUS...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11353</th>\n",
       "      <td>13-Jun-23</td>\n",
       "      <td>1.010000e+11</td>\n",
       "      <td>Enquiring about a downstairs toilet, states he...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11354</th>\n",
       "      <td>13-Jun-23</td>\n",
       "      <td>1.010000e+11</td>\n",
       "      <td>Phoning on behalf on her mother she has no fee...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11355</th>\n",
       "      <td>13-Jun-23</td>\n",
       "      <td>1.010000e+11</td>\n",
       "      <td>States on saturday they had to call an ambulan...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11356</th>\n",
       "      <td>13-Jun-23</td>\n",
       "      <td>1.010000e+11</td>\n",
       "      <td>Nadia Obaji daughter of Abdel Obaji contacted ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11357</th>\n",
       "      <td>13-Jun-23</td>\n",
       "      <td>1.010000e+11</td>\n",
       "      <td>Louise Hancock contacted in. \\n\\nShe has recen...</td>\n",
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       "</table>\n",
       "<p>11358 rows × 3 columns</p>\n",
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       "            Date            ID  \\\n",
       "0      26-Jul-17  1.010040e+11   \n",
       "1      28-Jul-17  1.010040e+11   \n",
       "2      28-Jul-17  1.010040e+11   \n",
       "3      31-Jul-17  1.010040e+11   \n",
       "4      28-Jul-17  1.010040e+11   \n",
       "...          ...           ...   \n",
       "11353  13-Jun-23  1.010000e+11   \n",
       "11354  13-Jun-23  1.010000e+11   \n",
       "11355  13-Jun-23  1.010000e+11   \n",
       "11356  13-Jun-23  1.010000e+11   \n",
       "11357  13-Jun-23  1.010000e+11   \n",
       "\n",
       "                                                    Text  \n",
       "0      please call daughter Dianne Thomas 07920 07565...  \n",
       "1      please speak to sister - Alyson Powell on abpv...  \n",
       "2      KELLY HAS RUNG - HER PARENTS ARE DUE TO HAVE W...  \n",
       "3      Mark Hitchings, Scheme Manager Swn Yr Afon con...  \n",
       "4      SON BRIAN HASFORD RUN G- HE IS RESIDENT IN AUS...  \n",
       "...                                                  ...  \n",
       "11353  Enquiring about a downstairs toilet, states he...  \n",
       "11354  Phoning on behalf on her mother she has no fee...  \n",
       "11355  States on saturday they had to call an ambulan...  \n",
       "11356  Nadia Obaji daughter of Abdel Obaji contacted ...  \n",
       "11357  Louise Hancock contacted in. \\n\\nShe has recen...  \n",
       "\n",
       "[11358 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Combined_SPA_Callback.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0047124b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Checking if a sentence is in caps:\n",
    "def calculate_capital_percentage(string):\n",
    "    total_letters = len(re.sub(r'[^a-zA-Z]', '', string))\n",
    "    capital_letters = len(re.sub(r'[^A-Z]', '', string))\n",
    "    capital_percentage = (capital_letters / total_letters) * 100\n",
    "    return capital_percentage\n",
    "\n",
    "# Function to clean text for NER:\n",
    "def clean_text_for_NER(text):\n",
    "    # Remove start/trailing spaces:\n",
    "    text = text.strip()\n",
    "    # Remove newline characters:\n",
    "    text = re.sub(r'[\\n\\r]+', '. ',text)\n",
    "    # Only keep Alphabets, Digits, Spaces, and Commonly Used Punctuations:\n",
    "    text = re.sub(r'[^a-zA-Z0-9\\s?,:\"!.\\']', '', text)\n",
    "    # Remove extra spaces:\n",
    "    text = re.sub(r'\\s{2,}', ' ', text)\n",
    "    # Join digits together:\n",
    "    text = re.sub('(?<=\\d) (?=\\d)', '', text)\n",
    "    text = text.strip()\n",
    "    if calculate_capital_percentage(text) > 65:\n",
    "        text = text.lower()\n",
    "    # Capitalize the first character:\n",
    "    text = text.capitalize()\n",
    "    return text\n",
    "\n",
    "# Creating a list of sentences:\n",
    "text_data = list(df[\"Text\"])\n",
    "\n",
    "# Clean the text\n",
    "cleaned_data = [clean_text_for_NER(text) for text in text_data]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f45a7e5",
   "metadata": {},
   "source": [
    "### Working with Spacy to Generate Triplets (Subject Phrase + Verb + Object Phrase):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c43d114a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This cell takes ~3 mins to run:\n",
    "\n",
    "# Function to perform NER using Spacy:\n",
    "def perform_spacy_ner(text):\n",
    "    named_entities = nlp(text)\n",
    "    return named_entities\n",
    "\n",
    "# Perform NER to extract sentences:\n",
    "ner_data = [perform_spacy_ner(text) for text in cleaned_data]\n",
    "\n",
    "# Perform NER on the extracted sentences:\n",
    "ner_complete = []\n",
    "for data_point in ner_data:\n",
    "    ner_sentences = [perform_spacy_ner(str(sent)) for sent in data_point.sents]\n",
    "    ner_complete.append(ner_sentences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "951f56bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting Triplets:\n",
    "\n",
    "triplets_results = []\n",
    "for datapoint in ner_complete:\n",
    "    datapoint_triplet = []\n",
    "    for sentence_ner in datapoint:\n",
    "        # Get Noun Phrases:\n",
    "        noun_phrases = [chunk.text for chunk in sentence_ner.noun_chunks]\n",
    "\n",
    "        # Get the root of the dependency tree:\n",
    "        root_token = [token for token in sentence_ner if token.head == token][0]\n",
    "\n",
    "        # Recursive function to traverse the tree:\n",
    "        def traverse_tree(token, depth=0):\n",
    "            if token.dep_ == \"ROOT\":\n",
    "                verb = token.text\n",
    "            if \"subj\" in token.dep_:\n",
    "                subjects.append(token.text)\n",
    "            if \"dobj\" in token.dep_:\n",
    "                objects.append(token.text)\n",
    "            if depth == 0:\n",
    "                for child in token.children:\n",
    "                    traverse_tree(child, depth + 1)\n",
    "            return subjects, objects\n",
    "\n",
    "        # Traversing the tree via Root Token (Verb):\n",
    "        subjects = []\n",
    "        objects = []\n",
    "        subjects, objects = traverse_tree(root_token)\n",
    "\n",
    "        # Getting Subject Phrases:\n",
    "        subject_phrases = [phrase for phrase in noun_phrases if any(subj in phrase for subj in subjects)] \\\n",
    "                          + [subj for subj in subjects if not any(subj in phrase for phrase in noun_phrases)]\n",
    "\n",
    "        # Getting Object Phrases:\n",
    "        object_phrases = [phrase for phrase in noun_phrases if any(obj in phrase for obj in objects)] \\\n",
    "                         + [obj for obj in objects if not any(obj in phrase for phrase in noun_phrases)]\n",
    "\n",
    "        # Create a sentence triplet using Subject Phrases + Verb + Object Phrases:\n",
    "        sentence_triplet = [\" \".join(subject_phrases), root_token.text, \" \".join(object_phrases)]\n",
    "\n",
    "        # Append the sentence triplets into a datapoint triplet:\n",
    "        datapoint_triplet.append(sentence_triplet)\n",
    "        \n",
    "    # Append the datapoint triplet into results:\n",
    "    triplets_results.append(datapoint_triplet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2576bf88",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to tokenize text:\n",
    "def tokenize_text(text):\n",
    "    # Tokenize sentences:\n",
    "    sentences = sent_tokenize(text)\n",
    "    # Tokenize words:\n",
    "    words = []\n",
    "    for sentence in sentences:\n",
    "        words += word_tokenize(sentence)\n",
    "    return words\n",
    "\n",
    "# Function to remove stopwords:\n",
    "def remove_stopwords(words):\n",
    "    stop_words = set(stopwords.words('english'))\n",
    "    words = [word for word in words if word not in stop_words]\n",
    "    return words\n",
    "\n",
    "# Function to convert tokens into original text:\n",
    "def words_to_text(words):\n",
    "    current_text = ' '.join(words)\n",
    "    return(current_text)\n",
    "\n",
    "# Function to lemmatize words:\n",
    "def lemmatize_words(words):\n",
    "    lemmatizer = WordNetLemmatizer()\n",
    "    lemmatized_words = []\n",
    "    for word, tag in pos_tag(words):\n",
    "        # Map POS tag to first letter used by WordNetLemmatizer:\n",
    "        tag = tag[0].lower() if tag[0].lower() in ['a', 'r', 'n', 'v'] else wordnet.NOUN\n",
    "        lemmatized_words.append(lemmatizer.lemmatize(word, tag))\n",
    "    return lemmatized_words\n",
    "\n",
    "# Function to stem words:\n",
    "def stem_words(words):\n",
    "    stemmer = PorterStemmer()\n",
    "    stemmed_words = []\n",
    "    for word in words:\n",
    "        stemmed_words.append(stemmer.stem(word))\n",
    "    return stemmed_words\n",
    "\n",
    "# Function to perform NER using NLTK:\n",
    "def perform_ner(text):\n",
    "    named_entities = ne_chunk(pos_tag(word_tokenize(text)))\n",
    "    return named_entities\n",
    "\n",
    "# Function to extract sentiment:\n",
    "def extract_sentiment(text):\n",
    "    sia = SentimentIntensityAnalyzer()\n",
    "    sentiment = sia.polarity_scores(text)\n",
    "    return sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "73f484e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform NER\n",
    "ner_data = [perform_ner(text) for text in cleaned_data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a5b34986",
   "metadata": {},
   "outputs": [
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y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">is</text></svg><svg width=\"100%\" x=\"0%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">VBZ</text></svg></svg><line stroke=\"black\" x1=\"50%\" x2=\"50%\" y1=\"1.2em\" y2=\"3em\" /></svg><line stroke=\"black\" x1=\"50%\" x2=\"58.7379%\" y1=\"1.2em\" y2=\"3em\" /><svg width=\"8.73786%\" x=\"61.165%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">looking</text></svg><svg width=\"100%\" x=\"0%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">VBG</text></svg></svg><line stroke=\"black\" x1=\"50%\" x2=\"50%\" y1=\"1.2em\" y2=\"3em\" /></svg><line stroke=\"black\" x1=\"50%\" x2=\"65.534%\" y1=\"1.2em\" y2=\"3em\" /><svg width=\"4.85437%\" x=\"69.9029%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">for</text></svg><svg width=\"100%\" x=\"0%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">IN</text></svg></svg><line stroke=\"black\" x1=\"50%\" x2=\"50%\" y1=\"1.2em\" y2=\"3em\" /></svg><line stroke=\"black\" x1=\"50%\" x2=\"72.3301%\" y1=\"1.2em\" y2=\"3em\" /><svg width=\"8.73786%\" x=\"74.7573%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">support</text></svg><svg width=\"100%\" x=\"0%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">NN</text></svg></svg><line stroke=\"black\" x1=\"50%\" x2=\"50%\" y1=\"1.2em\" y2=\"3em\" /></svg><line stroke=\"black\" x1=\"50%\" x2=\"79.1262%\" y1=\"1.2em\" y2=\"3em\" /><svg width=\"5.82524%\" x=\"83.4951%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">with</text></svg><svg width=\"100%\" x=\"0%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">IN</text></svg></svg><line stroke=\"black\" x1=\"50%\" x2=\"50%\" y1=\"1.2em\" y2=\"3em\" /></svg><line stroke=\"black\" x1=\"50%\" x2=\"86.4078%\" y1=\"1.2em\" y2=\"3em\" /><svg width=\"5.82524%\" x=\"89.3204%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">her</text></svg><svg width=\"100%\" x=\"0%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">PRP$</text></svg></svg><line stroke=\"black\" x1=\"50%\" x2=\"50%\" y1=\"1.2em\" y2=\"3em\" /></svg><line stroke=\"black\" x1=\"50%\" x2=\"92.233%\" y1=\"1.2em\" y2=\"3em\" /><svg width=\"4.85437%\" x=\"95.1456%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">mum</text></svg><svg width=\"100%\" x=\"0%\" y=\"3em\"><defs /><svg width=\"100%\" x=\"0\" y=\"0em\"><defs /><text text-anchor=\"middle\" x=\"50%\" y=\"1em\">NN</text></svg></svg><line stroke=\"black\" x1=\"50%\" x2=\"50%\" y1=\"1.2em\" y2=\"3em\" /></svg><line stroke=\"black\" x1=\"50%\" x2=\"97.5728%\" y1=\"1.2em\" y2=\"3em\" /></svg>"
      ],
      "text/plain": [
       "Tree('S', [('Please', 'VB'), ('call', 'JJ'), ('daughter', 'NN'), ('dianne', 'NN'), ('thomas', 'NN'), ('07920075650', 'CD'), ('she', 'PRP'), ('is', 'VBZ'), ('looking', 'VBG'), ('for', 'IN'), ('support', 'NN'), ('with', 'IN'), ('her', 'PRP$'), ('mum', 'NN')])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Same example in an NER tree form:\n",
    "ner_data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "205c7e49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract named entities and their labels:\n",
    "\n",
    "organizations = []\n",
    "persons = []\n",
    "geopoliticals = []\n",
    "locations = []\n",
    "ner_stopwords = [\"Please\",\"PLEASE\",\"URGENT\",\"ONLY\",\"ASAP\",\"CALLBACK\",\"CALL\"]\n",
    "\n",
    "entities = []\n",
    "labels = []\n",
    "for doc in ner_data:\n",
    "    for chunk in doc:\n",
    "        if hasattr(chunk, 'label'):\n",
    "            flag = 0\n",
    "            if chunk.label()==\"ORGANIZATION\":\n",
    "                organization = ' '.join(c[0] for c in chunk)\n",
    "                if organization not in ner_stopwords:\n",
    "                    organizations.append(organization)\n",
    "                    flag = 1 \n",
    "            elif chunk.label()==\"PERSON\":\n",
    "                person = ' '.join(c[0] for c in chunk)\n",
    "                if len(person)>2 and person not in ner_stopwords:\n",
    "                    persons.append(' '.join(c[0] for c in chunk))\n",
    "                    flag = 1\n",
    "            if flag == 1:\n",
    "                entities.append(' '.join(c[0] for c in chunk))\n",
    "                labels.append(chunk.label())\n",
    "\n",
    "# Count the frequency of each label:\n",
    "label_counts = Counter(labels)\n",
    "\n",
    "# Plot the frequency of each label:\n",
    "plt.bar(label_counts.keys(), label_counts.values())\n",
    "plt.title('Distibution of Entities')\n",
    "plt.xlabel('Entity Label')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bf54bcc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tokenize text\n",
    "tokenized_data = [tokenize_text(text) for text in cleaned_data]\n",
    "\n",
    "# Remove stopwords\n",
    "no_stopwords_data = [remove_stopwords(words) for words in tokenized_data]\n",
    "\n",
    "# Lemmatize words\n",
    "lemmatized_data = [lemmatize_words(words) for words in no_stopwords_data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3f57b061",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating word distribution:\n",
    "no_of_tokens = [len(sentence) for sentence in lemmatized_data]\n",
    "bin_width = 5\n",
    "threshold = 150\n",
    "\n",
    "# Calculate the bin edges:\n",
    "bins = list(range(min(no_of_tokens)-1, threshold + bin_width, bin_width)) + [max(no_of_tokens) + bin_width]\n",
    "\n",
    "# Plotting the distribution as a histogram:\n",
    "plt.hist(no_of_tokens, bins=bins)\n",
    "plt.xlabel('Number of Words')\n",
    "plt.ylabel('Sentences')\n",
    "plt.title('Distribution of Words')\n",
    "\n",
    "# Setting x-axis limits and showing plot:\n",
    "plt.xlim(0, 160)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b09fb22e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 most common occurences of Organizations/ Products:\n",
      "Commode: 1\n",
      "\n",
      "Top 10 most common occurences of Persons:\n",
      "Client: 149\n",
      "Son: 83\n",
      "Cust: 73\n",
      "Daughter: 42\n",
      "Wife: 13\n",
      "Sister: 11\n",
      "David: 8\n",
      "John: 7\n",
      "Clients: 7\n",
      "Lady: 7\n"
     ]
    }
   ],
   "source": [
    "# Sort by rank (most common to least common)\n",
    "sorted_counter = sorted(Counter(organizations).items(), key=lambda x: x[1], reverse=True)\n",
    "print(\"Top 10 most common occurences of Organizations/ Products:\")\n",
    "for item, count in sorted_counter[:10]:\n",
    "    print('{}: {}'.format(item,count))\n",
    "    \n",
    "# Sort by rank (most common to least common)\n",
    "sorted_counter = sorted(Counter(persons).items(), key=lambda x: x[1], reverse=True)\n",
    "print(\"\\nTop 10 most common occurences of Persons:\")\n",
    "for item, count in sorted_counter[:10]:\n",
    "    print('{}: {}'.format(item,count))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a816b46e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}