{
 "metadata": {
  "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
  },
  "orig_nbformat": 4,
  "coopTranslator": {
   "original_hash": "27de2abc0235ebd22080fc8f1107454d",
   "translation_date": "2025-08-29T15:43:46+00:00",
   "source_file": "6-NLP/3-Translation-Sentiment/solution/notebook.ipynb",
   "language_code": "ar"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from textblob import TextBlob\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You should download the book text, clean it, and import it here\n",
    "with open(\"pride.txt\", encoding=\"utf8\") as f:\n",
    "    file_contents = f.read()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "book_pride = TextBlob(file_contents)\n",
    "positive_sentiment_sentences = []\n",
    "negative_sentiment_sentences = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for sentence in book_pride.sentences:\n",
    "    if sentence.sentiment.polarity == 1:\n",
    "        positive_sentiment_sentences.append(sentence)\n",
    "    if sentence.sentiment.polarity == -1:\n",
    "        negative_sentiment_sentences.append(sentence)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"The \" + str(len(positive_sentiment_sentences)) + \" most positive sentences:\")\n",
    "for sentence in positive_sentiment_sentences:\n",
    "    print(\"+ \" + str(sentence.replace(\"\\n\", \"\").replace(\"      \", \" \")))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"The \" + str(len(negative_sentiment_sentences)) + \" most negative sentences:\")\n",
    "for sentence in negative_sentiment_sentences:\n",
    "    print(\"- \" + str(sentence.replace(\"\\n\", \"\").replace(\"      \", \" \")))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n---\n\n**إخلاء المسؤولية**:  \nتم ترجمة هذا المستند باستخدام خدمة الترجمة بالذكاء الاصطناعي [Co-op Translator](https://github.com/Azure/co-op-translator). بينما نسعى لتحقيق الدقة، يرجى العلم أن الترجمات الآلية قد تحتوي على أخطاء أو معلومات غير دقيقة. يجب اعتبار المستند الأصلي بلغته الأصلية المصدر الموثوق. للحصول على معلومات حاسمة، يُوصى بالاستعانة بترجمة بشرية احترافية. نحن غير مسؤولين عن أي سوء فهم أو تفسيرات خاطئة ناتجة عن استخدام هذه الترجمة.\n"
   ]
  }
 ]
}