vader-sentiment-analysis.ipynb

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   "source": [
    "# How to deploy a Sentiment Analysis App \n",
    "## with Vader and Plotly Dash"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a63d832",
   "metadata": {},
   "source": [
    "#### About Vader\n",
    "* https://www.analyticsvidhya.com/blog/2021/06/vader-for-sentiment-analysis/\n",
    "* https://www.geeksforgeeks.org/python-sentiment-analysis-using-vader/\n",
    "* https://pypi.org/project/vaderSentiment/\n",
    "* https://github.com/cjhutto/vaderSentiment\n"
   ]
  },
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     "text": [
      "\n",
      "1st statement :\n",
      "Overall sentiment dictionary is :  {'neg': 0.165, 'neu': 0.588, 'pos': 0.247, 'compound': 0.5267}\n",
      "sentence was rated as  16.5 % Negative\n",
      "sentence was rated as  58.8 % Neutral\n",
      "sentence was rated as  24.7 % Positive\n",
      "Sentence Overall Rated As Positive\n",
      "\n",
      "2nd Statement :\n",
      "Overall sentiment dictionary is :  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}\n",
      "sentence was rated as  0.0 % Negative\n",
      "sentence was rated as  100.0 % Neutral\n",
      "sentence was rated as  0.0 % Positive\n",
      "Sentence Overall Rated As Neutral\n",
      "\n",
      "3rd Statement :\n",
      "Overall sentiment dictionary is :  {'neg': 0.459, 'neu': 0.541, 'pos': 0.0, 'compound': -0.5256}\n",
      "sentence was rated as  45.9 % Negative\n",
      "sentence was rated as  54.1 % Neutral\n",
      "sentence was rated as  0.0 % Positive\n",
      "Sentence Overall Rated As Negative\n"
     ]
    }
   ],
   "source": [
    "# https://www.geeksforgeeks.org/python-sentiment-analysis-using-vader/\n",
    "# import SentimentIntensityAnalyzer class\n",
    "# from vaderSentiment.vaderSentiment module.\n",
    "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
    " \n",
    "# function to print sentiments\n",
    "# of the sentence.\n",
    "def sentiment_scores(sentence):\n",
    " \n",
    "    # Create a SentimentIntensityAnalyzer object.\n",
    "    sid_obj = SentimentIntensityAnalyzer()\n",
    " \n",
    "    # polarity_scores method of SentimentIntensityAnalyzer\n",
    "    # object gives a sentiment dictionary.\n",
    "    # which contains pos, neg, neu, and compound scores.\n",
    "    sentiment_dict = sid_obj.polarity_scores(sentence)\n",
    "     \n",
    "    print(\"Overall sentiment dictionary is : \", sentiment_dict)\n",
    "    print(\"sentence was rated as \", sentiment_dict['neg']*100, \"% Negative\")\n",
    "    print(\"sentence was rated as \", sentiment_dict['neu']*100, \"% Neutral\")\n",
    "    print(\"sentence was rated as \", sentiment_dict['pos']*100, \"% Positive\")\n",
    " \n",
    "    print(\"Sentence Overall Rated As\", end = \" \")\n",
    " \n",
    "    # decide sentiment as positive, negative and neutral\n",
    "    if sentiment_dict['compound'] >= 0.05 :\n",
    "        print(\"Positive\")\n",
    " \n",
    "    elif sentiment_dict['compound'] <= - 0.05 :\n",
    "        print(\"Negative\")\n",
    " \n",
    "    else :\n",
    "        print(\"Neutral\")\n",
    " \n",
    " \n",
    "   \n",
    "# Driver code\n",
    "if __name__ == \"__main__\" :\n",
    " \n",
    "    print(\"\\n1st statement :\")\n",
    "    sentence = \"Geeks For Geeks is the best portal for \\\n",
    "                the computer science engineering students.\"\n",
    " \n",
    "    # function calling\n",
    "    sentiment_scores(sentence)\n",
    " \n",
    "    print(\"\\n2nd Statement :\")\n",
    "    sentence = \"study is going on as usual\"\n",
    "    sentiment_scores(sentence)\n",
    " \n",
    "    print(\"\\n3rd Statement :\")\n",
    "    sentence = \"I am very sad today.\"\n",
    "    sentiment_scores(sentence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3a271531",
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    {
     "name": "stdout",
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     "text": [
      "Overall sentiment dictionary is :  {'neg': 0.57, 'neu': 0.43, 'pos': 0.0, 'compound': -0.9393}\n",
      "sentence was rated as  56.99999999999999 % Negative\n",
      "sentence was rated as  43.0 % Neutral\n",
      "sentence was rated as  0.0 % Positive\n",
      "Sentence Overall Rated As Negative\n"
     ]
    }
   ],
   "source": [
    "sentence = 'You have controlled your fear. Now, release your anger. Only your hatred can destroy me.'\n",
    "sentiment_scores(sentence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "087c657f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text Selected for VADER Sentimental Analysis :\n",
      "ANN is like our brain; millions and billions of cells — called neurons, which processes information in the form of electric signals. Similarly, in ANN, the network structure has an input layer, a hidden layer, and the output layer. It is also called Multi-Layer Perceptron as it has multiple layers. The hidden layer is known as a “distillation layer” that distils some critical patterns from the data/information and passes it onto the next layer. It then makes the network quicker and more productive by distinguishing the data from the data sources, leaving out the excess data.\n",
      "Overall sentiment dictionary is :  {'neg': 0.023, 'neu': 0.951, 'pos': 0.025, 'compound': 0.0516}\n",
      "sentence was rated as  2.3 % Negative\n",
      "sentence was rated as  95.1 % Neutral\n",
      "sentence was rated as  2.5 % Positive\n",
      "Sentence Overall Rated As Positive\n"
     ]
    }
   ],
   "source": [
    "# https://www.analyticsvidhya.com/blog/2021/06/vader-for-sentiment-analysis/\n",
    "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
    "# function to print sentiments\n",
    "# of the sentence.\n",
    "def sentiment_scores(sentence):\n",
    "# Create a SentimentIntensityAnalyzer object.\n",
    "    sid_obj = SentimentIntensityAnalyzer()\n",
    "# polarity_scores method of SentimentIntensityAnalyzer\n",
    "# oject gives a sentiment dictionary.\n",
    "# which contains pos, neg, neu, and compound scores.\n",
    "    sentiment_dict = sid_obj.polarity_scores(sentence)\n",
    "    print(\"Overall sentiment dictionary is : \", sentiment_dict)\n",
    "    print(\"sentence was rated as \", sentiment_dict['neg']*100, \"% Negative\")\n",
    "    print(\"sentence was rated as \", sentiment_dict['neu']*100, \"% Neutral\")\n",
    "    print(\"sentence was rated as \", sentiment_dict['pos']*100, \"% Positive\")\n",
    "    print(\"Sentence Overall Rated As\", end = \" \")\n",
    "    # decide sentiment as positive, negative and neutral\n",
    "    if sentiment_dict['compound'] >= 0.05 :\n",
    "        print(\"Positive\")\n",
    "    elif sentiment_dict['compound'] <= - 0.05 :\n",
    "        print(\"Negative\")\n",
    "    else :\n",
    "        print(\"Neutral\")\n",
    "\n",
    "print(\"Text Selected for VADER Sentimental Analysis :\")\n",
    "sentence1 = ('''ANN is like our brain; millions and billions of cells — called neurons, which processes information in the form of electric signals. Similarly, in ANN, the network structure has an input layer, a hidden layer, and the output layer. It is also called Multi-Layer Perceptron as it has multiple layers. The hidden layer is known as a “distillation layer” that distils some critical patterns from the data/information and passes it onto the next layer. It then makes the network quicker and more productive by distinguishing the data from the data sources, leaving out the excess data.''')\n",
    "print(sentence1)\n",
    "sentiment_scores(sentence1)"
   ]
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