nlp_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "33dfee7f-d2ff-4f6e-bad7-931a5962d3aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Sample class names\n",
    "classes = ['Cat', 'Dog', 'Rabbit', 'Bird', 'Fish']\n",
    "\n",
    "# Generating mock true labels and predictions\n",
    "np.random.seed(0) # For reproducibility\n",
    "y_test = np.random.choice(classes, 100, replace=True) # True labels\n",
    "predictions = np.random.choice(classes, 100, replace=True) # Predicted labels\n",
    "\n",
    "# Generating the confusion matrix\n",
    "cm = confusion_matrix(y_test, predictions, labels=classes)\n",
    "\n",
    "# Plotting the confusion matrix\n",
    "plt.figure(figsize=(10, 7))\n",
    "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=classes, yticklabels=classes)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b16ae5b5-0928-441f-b8ec-c941f2c82709",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I need to ask about extending the period of my loan repayment.\n",
      "Could the credit duration be lengthened due to unforeseen circumstances?\n",
      "Is there a possibility for my money borrowing term to be prolonged?\n",
      "We are considering applying for additional funding to extend our credit line.\n",
      "I'm facing financial difficulties and need to extend the credit agreement term.\n",
      "I need a deferment for the money I owe; is that something we can discuss?\n"
     ]
    }
   ],
   "source": [
    "import spacy\n",
    "\n",
    "nlp = spacy.load(\"en_core_web_sm\")\n",
    "\n",
    "sentences = [\n",
    "    \"I need to ask about extending the period of my loan repayment.\",\n",
    "    \"Could the credit duration be lengthened due to unforeseen circumstances?\",\n",
    "    \"Is there a possibility for my money borrowing term to be prolonged?\",\n",
    "    \"I'm inquiring about the possibility of having more time to pay back my loan.\",\n",
    "    \"We are considering applying for additional funding to extend our credit line.\",\n",
    "    \"Would it be feasible to delay the repayment schedule for my current loan?\",\n",
    "    \"I'm facing financial difficulties and need to extend the credit agreement term.\",\n",
    "    \"How can I go about securing a longer period for my money lending contract?\",\n",
    "    \"The bank offered a possibility to expand the duration of my loan repayment plan.\",\n",
    "    \"I need a deferment for the money I owe; is that something we can discuss?\"\n",
    "]\n",
    "\n",
    "# Define a function to check if any lemmas from both lists appear in the same sentence\n",
    "def lemma_in_same_sentence(doc, lemma1_list, lemma2_list):\n",
    "    sentence_lemmas = [token.lemma_ for token in doc]\n",
    "    return any(lemma1 in sentence_lemmas for lemma1 in lemma1_list) and any(lemma2 in sentence_lemmas for lemma2 in lemma2_list)\n",
    "\n",
    "# Define your alternatives for lemma1 and lemma2\n",
    "lemma1_alternatives = ['loan', 'credit', 'money']\n",
    "lemma2_alternatives = ['extend', 'lengthen', 'prolong', 'deferment']\n",
    "\n",
    "# Analyze sentences\n",
    "matches = []\n",
    "for sentence in sentences:\n",
    "    doc = nlp(sentence)\n",
    "    if lemma_in_same_sentence(doc, lemma1_alternatives, lemma2_alternatives):\n",
    "        matches.append(sentence)\n",
    "\n",
    "# Print out matches\n",
    "for match in matches:\n",
    "    print(match)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "530a25f5-c3ca-49b9-aed4-09a9e293cb5d",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}