Question_2_AA_1.ipynb

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     "name": "stdout",
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     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "HaTk9ozgyLFj"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import spacy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "NqBHRAvzyCyW"
   },
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   "source": [
    "df =pd.read_csv(\"/content/drive/MyDrive/NLP/example.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "UVBy3MsXyC-E"
   },
   "outputs": [],
   "source": [
    "filtered_df = df[df['entity_context'].apply(lambda x: x != '[]' and bool(x))]\n",
    "filtered_df = filtered_df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "mCjzaW-CyhTU"
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   "source": [
    "entity_context_data = filtered_df['entity_context']"
   ]
  },
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    "valence = pd.read_csv(\"/content/drive/MyDrive/NLP/valence-NRC-VAD-Lexicon.txt\", sep=\"\\t\", header=None, names=[\"word\", \"score\"])"
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       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      buttonEl.onclick = () => {\n",
       "        google.colab.notebook.generateWithVariable('valence');\n",
       "      }\n",
       "      })();\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "                word  score\n",
       "0           generous  1.000\n",
       "1               love  1.000\n",
       "2      very positive  1.000\n",
       "3        magnificent  1.000\n",
       "4            happily  1.000\n",
       "...              ...    ...\n",
       "19966  disheartening  0.010\n",
       "19967     mistreated  0.010\n",
       "19968          toxic  0.008\n",
       "19969      nightmare  0.005\n",
       "19970           shit  0.000\n",
       "\n",
       "[19971 rows x 2 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "wTRJi3t-3mTp"
   },
   "outputs": [],
   "source": [
    "valence = valence.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DeeACmDc43QH",
    "outputId": "dbc015ab-eb3b-42b4-c5a4-7ab65c95624e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 19970 entries, 0 to 19970\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   word    19970 non-null  object \n",
      " 1   score   19970 non-null  float64\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 468.0+ KB\n"
     ]
    }
   ],
   "source": [
    "valence.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2eOPbQeF5xGO"
   },
   "outputs": [],
   "source": [
    "valence = valence.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "DrxFpMSE7Nz1"
   },
   "outputs": [],
   "source": [
    "lexicon_dict = dict(zip(valence['word'], valence['score']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6u8IVssY7Spd"
   },
   "outputs": [],
   "source": [
    "nlp = spacy.load(\"en_core_web_sm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9dGZdlEv9mAk"
   },
   "outputs": [],
   "source": [
    "def clean_text(text):\n",
    "    cleaned_text = text.strip(\"[]\").replace(\"'\", \"\").strip()\n",
    "    return cleaned_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "PStuEbeV9qdb"
   },
   "outputs": [],
   "source": [
    "entity_context = [clean_text(entity) for entity in entity_context_data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "gje6LWj07dpN"
   },
   "outputs": [],
   "source": [
    "entities_to_analyze = [\"hamas\", \"israel\"]\n",
    "results = {\"hamas\": [], \"israel\": []}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "0iPmzB6Y-xhK"
   },
   "outputs": [],
   "source": [
    "def analyze_entity_sentiment(text, entity, lexicon):\n",
    "    doc = nlp(text)\n",
    "    entity_verbs_modifiers = []\n",
    "\n",
    "    for token in doc:\n",
    "        if token.text == entity and token.dep_ == \"nsubj\":\n",
    "            verbs = [child.text for child in token.children if child.pos_ == \"VERB\"]\n",
    "            modifiers = [child.text for child in token.children if child.pos_ in [\"ADJ\", \"ADV\"]]\n",
    "            entity_verbs_modifiers.extend(verbs + modifiers)\n",
    "\n",
    "    sentiment_scores = [lexicon[word] for word in entity_verbs_modifiers if word in lexicon]\n",
    "    if sentiment_scores:\n",
    "        avg_sentiment = sum(sentiment_scores) / len(sentiment_scores)\n",
    "    else:\n",
    "        avg_sentiment = None\n",
    "\n",
    "    return {\n",
    "        \"entity\": entity,\n",
    "        \"words\": entity_verbs_modifiers,\n",
    "        \"average_sentiment\": avg_sentiment\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "gJWbSuxg7dvt"
   },
   "outputs": [],
   "source": [
    "for text in entity_context:\n",
    "  for entity in entities_to_analyze:\n",
    "    result = analyze_entity_sentiment(text, entity, lexicon_dict)\n",
    "    if result['average_sentiment'] is not None:\n",
    "            results[entity].append(result['average_sentiment'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "RNzWjJdimAKc"
   },
   "outputs": [],
   "source": [
    "hamas_avg_sentiment = sum(results['hamas']) / len(results['hamas'])\n",
    "israel_avg_sentiment = sum(results['israel']) / len(results['israel'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5d_zvfgS7dy0",
    "outputId": "68b6b08a-bf45-48f8-da8c-b041a638f194"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average sentiment for 'hamas': 0.5412050473186111\n",
      "Average sentiment for 'israel': 0.5556480280929228\n"
     ]
    }
   ],
   "source": [
    "print(f\"Average sentiment for 'hamas': {hamas_avg_sentiment}\")\n",
    "print(f\"Average sentiment for 'israel': {israel_avg_sentiment}\")"
   ]
  }
 ],
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