web_scraping_and_data_anal.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPQAB7ZsseHJwmfik6zExK2",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/shlok-py/fox_news_Data_analysis_web_scraping/blob/main/web_scraping_and_data_anal.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this program \"*web_Scraping_and_data_anal.py*\" I scraped out some key data from foxnews.com and did data analysis on its news, sentimental analysis, frequency analysis and many more.\n",
        "Now, let's begin this.\n",
        "\n",
        "# STEP 1. Importing Dependencies\n"
      ],
      "metadata": {
        "id": "Ys6bAkkfYpZD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from bs4 import BeautifulSoup\n",
        "import requests"
      ],
      "metadata": {
        "id": "jYiTqoZLZ0u9"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.2 Giving the static URL \n",
        "And using request response to get the html"
      ],
      "metadata": {
        "id": "hsKc3N88ZhgX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "url = \"https://www.foxnews.com/\"\n",
        "response = requests.get(url).text"
      ],
      "metadata": {
        "id": "MH1N77ISwvLi"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.3 Parsing response using html.parser"
      ],
      "metadata": {
        "id": "woFui6yQZ1aK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "soup = BeautifulSoup(response, 'html.parser')"
      ],
      "metadata": {
        "id": "OvA7SNkuaxIF"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.4 checking if the code works using soup.title"
      ],
      "metadata": {
        "id": "BflDPM0JaTyO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "soup.title"
      ],
      "metadata": {
        "id": "LH4fR5sMa_Vn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "af606c33-155b-4c96-87ec-6f37984f8e58"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<title>Fox News - Breaking News Updates | Latest News Headlines | Photos &amp; News Videos</title>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#STEP 2. Scrape the title from the homepage from foxnews.com"
      ],
      "metadata": {
        "id": "OYHTiunVaaZ2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#scrape the title\n",
        "titles = soup.find_all('h3', class_= 'title')\n",
        "news_titles = []\n",
        "for title in titles:\n",
        "    anchor_tag = title.find('a')\n",
        "    if anchor_tag == None:\n",
        "      news_titles.append(None)\n",
        "      continue\n",
        "    print(anchor_tag.text.strip())\n",
        "    news_titles.append(anchor_tag.text.strip())"
      ],
      "metadata": {
        "id": "le73zpCSw-B3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#STEP 3. Scrape the URL from the homepage from foxnews.com"
      ],
      "metadata": {
        "id": "EydUcDSLakFT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#scrpe the url\n",
        "urls = []\n",
        "for link in titles:\n",
        "  url = link.find('a')\n",
        "  if url == None:\n",
        "    urls.append(None)\n",
        "    continue\n",
        "  urls.append(url['href'])\n",
        "for i in range(len(urls)):\n",
        "  if urls[i]!=None:\n",
        "    if not urls[i].startswith('https:'):\n",
        "      urls[i] = 'https:' + urls[i]\n",
        "urls"
      ],
      "metadata": {
        "id": "6CxZQDWk7MS0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# STEP 4: Scrape the news Corpus"
      ],
      "metadata": {
        "id": "zg3LjZveapn1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#scrape the news corpus\n",
        "art_text = []\n",
        "for url in urls:\n",
        "  try:\n",
        "    response = requests.get(url).text\n",
        "    f_texts = BeautifulSoup(response, 'html.parser')\n",
        "    main_text = f_texts.find('div', class_='article-body')\n",
        "    if main_text == None:\n",
        "      art_text.append(None)\n",
        "      continue\n",
        "    article_text = main_text.get_text()\n",
        "    print(article_text.strip())\n",
        "    art_text.append(article_text.strip())\n",
        "  except requests.exceptions.RequestException as e:\n",
        "    # Handle the exception (e.g. log an error message, skip the URL, etc.)\n",
        "    print(f'Error while requesting {url}: {e}')\n",
        "    art_text.append(None)\n",
        "    continue\n",
        "len(art_text)"
      ],
      "metadata": {
        "id": "r1gnlj3_7OAO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# STEP 5: Scrape the news category"
      ],
      "metadata": {
        "id": "eaCh3dYBa9Qg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#find the news category\n",
        "from urllib.parse import urlparse\n",
        "news_cat = []\n",
        "for url in urls:\n",
        "  url_parts = urlparse(url)\n",
        "  print(url)\n",
        "  if url == None:\n",
        "    news_cat.append(None)\n",
        "    continue\n",
        "  path_parts = url_parts.path.split('/')\n",
        "  category = path_parts[1] if len(path_parts) > 1 else ' '\n",
        "  news_cat.append(category)"
      ],
      "metadata": {
        "id": "v3VplfEcMac7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# STEP 6: Scrape Authors Name"
      ],
      "metadata": {
        "id": "LaBO8HYIbEJe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#scraping the authors\n",
        "author_names = []\n",
        "for url in urls:\n",
        "  if url == None:\n",
        "    author_names.append(None)\n",
        "    continue\n",
        "  response = requests.get(url)\n",
        "\n",
        "  soup = BeautifulSoup(response.text, 'html.parser')\n",
        "\n",
        "  # Find the element containing the author name\n",
        "  author_element = soup.find('a', class_='author-byline')\n",
        "  if author_element:\n",
        "      # Extract the author name from the element\n",
        "      author_name = author_element.text.strip()\n",
        "      author_names.append(author_name)\n",
        "  else:\n",
        "      # If the author element is not found, set the author name to an empty string\n",
        "      author_name = ''\n",
        "      author_names.append(author_name)"
      ],
      "metadata": {
        "id": "97ZJR_O1OudW"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# STEP 7: Scrape date and time"
      ],
      "metadata": {
        "id": "-eESwpFuNc8x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Scrape the time and date\n",
        "time = []\n",
        "for url in urls:\n",
        "  if url == None:\n",
        "    time.append(None)\n",
        "    continue\n",
        "  response = requests.get(url)\n",
        "  soup = BeautifulSoup(response.text, 'html.parser')\n",
        "  news_dates = soup.find('time')\n",
        "  if news_dates == None:\n",
        "    time.append(None)\n",
        "    continue\n",
        "  time.append(news_dates.get_text())\n",
        "print(time)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v90uSXiJbr-2",
        "outputId": "46b25a5e-4a42-483f-8998-76b00b10240a"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[' March 19, 2023 9:59pm EDT', ' March 19, 2023 8:50pm EDT', ' March 19, 2023 6:16pm EDT', ' March 19, 2023 7:58pm EDT', ' March 19, 2023 8:04pm EDT', ' March 19, 2023 5:18pm EDT', ' March 19, 2023 3:45pm EDT', ' March 19, 2023 2:58pm EDT', ' March 20, 2023 12:03am EDT', ' March 19, 2023 3:54pm EDT', None, None, None, ' March 19, 2023 7:34pm EDT', ' March 19, 2023 4:56pm EDT', ' March 19, 2023 10:42am EDT', ' March 19, 2023 3:44pm EDT', ' March 19, 2023 4:00pm EDT', 'Published March 19, 2023 4:38pm EDT\\n        ', ' March 19, 2023 8:08pm EDT', ' March 19, 2023 12:46pm EDT', ' March 19, 2023 2:47pm EDT', ' March 19, 2023 10:28pm EDT', ' March 19, 2023 9:35pm EDT', ' March 19, 2023 9:55pm EDT', ' March 19, 2023 11:49pm EDT', 'Published March 19, 2023 11:26am EDT\\n        ', ' March 19, 2023 9:50pm EDT', ' March 19, 2023 5:17pm EDT', ' March 19, 2023 9:44pm EDT', ' March 19, 2023 4:22pm EDT', ' March 19, 2023 2:54pm EDT', ' March 19, 2023 9:11pm EDT', ' March 19, 2023 11:44am EDT', 'March 19, 2023', 'March 19, 2023', ' March 19, 2023 9:19pm EDT', ' March 17, 2023 2:00am EDT', ' March 20, 2023 12:02am EDT', '18 hours ago', None, ' March 19, 2023 7:30am EDT', ' March 19, 2023 5:30am EDT', ' March 18, 2023 12:00pm EDT', ' March 18, 2023 8:00am EDT', ' March 19, 2023 1:12pm EDT', ' March 18, 2023 9:00pm EDT', ' March 17, 2023 3:32pm EDT', ' March 17, 2023 2:17pm EDT', ' March 19, 2023 9:56pm EDT', ' March 19, 2023 8:50pm EDT', ' March 19, 2023 2:00pm EDT', ' March 19, 2023 12:00pm EDT', ' March 20, 2023 12:02am EDT', ' March 19, 2023 9:55pm EDT', ' March 19, 2023 8:04pm EDT', ' March 19, 2023 3:05pm EDT', ' March 19, 2023 4:22pm EDT', ' March 19, 2023 11:00am EDT', ' March 19, 2023 7:30am EDT', ' March 18, 2023 2:58pm EDT', ' March 19, 2023 9:35pm EDT', ' March 19, 2023 8:50pm EDT', ' March 19, 2023 7:34pm EDT', ' March 19, 2023 4:56pm EDT', ' March 19, 2023 9:00am EDT', ' March 19, 2023 8:00am EDT', ' March 18, 2023 10:00am EDT', ' March 18, 2023 9:00am EDT', ' March 19, 2023 3:19pm EDT', ' March 19, 2023 12:46pm EDT', ' March 18, 2023 5:36pm EDT', ' March 18, 2023 12:43pm EDT', ' March 20, 2023 12:03am EDT', ' March 19, 2023 9:19pm EDT', ' March 19, 2023 7:55pm EDT', ' March 19, 2023 5:48pm EDT', ' February 24, 2023 7:15am EST', ' February 2, 2023 11:08am EST', ' January 17, 2023 4:40pm EST', ' January 17, 2023 4:00am EST', ' March 19, 2023 6:11pm EDT', ' March 19, 2023 12:46pm EDT', ' March 19, 2023 8:00am EDT', ' March 19, 2023 5:39am EDT', ' March 19, 2023 11:49pm EDT', ' March 19, 2023 8:08pm EDT', ' March 19, 2023 6:14pm EDT', ' March 19, 2023 5:11pm EDT', ' March 20, 2023 12:31am EDT', ' March 19, 2023 11:39pm EDT', ' March 19, 2023 10:28pm EDT', ' March 19, 2023 10:04pm EDT', ' March 20, 2023 12:03am EDT', ' March 19, 2023 9:44pm EDT', ' March 19, 2023 9:19pm EDT', ' March 19, 2023 9:11pm EDT', ' March 19, 2023 9:00am EDT', ' March 19, 2023 8:00am EDT', ' March 18, 2023 5:36pm EDT', ' March 18, 2023 1:56pm EDT', 'Published March 18, 2023 7:00am EDT\\n        ', 'Published March 17, 2023 3:43pm EDT\\n        ', 'Published March 17, 2023 11:17am EDT\\n        ', 'Published March 17, 2023 9:48am EDT\\n        ', ' March 19, 2023 3:18pm EDT', ' March 19, 2023 9:27am EDT', ' March 19, 2023 9:04am EDT', ' March 18, 2023 1:11pm EDT', ' March 19, 2023 11:49pm EDT', ' March 19, 2023 6:14pm EDT', ' March 19, 2023 5:11pm EDT', ' March 19, 2023 4:25pm EDT', 'Published March 19, 2023 8:11pm EDT\\n        ', 'Published March 19, 2023 7:06pm EDT\\n        ', 'Published March 19, 2023 6:55pm EDT\\n        ', 'Published March 19, 2023 6:17pm EDT\\n        ', ' March 19, 2023 9:00am EDT', ' March 19, 2023 8:00am EDT', ' March 18, 2023 6:00pm EDT', ' March 18, 2023 5:36pm EDT', ' March 19, 2023 7:02pm EDT', ' March 19, 2023 4:00pm EDT', ' March 19, 2023 3:54pm EDT', ' March 19, 2023 2:30pm EDT', ' March 19, 2023 12:54pm EDT', ' March 19, 2023 9:00am EDT', ' March 18, 2023 9:59pm EDT', ' March 18, 2023 9:41pm EDT', ' March 19, 2023 8:08pm EDT', ' March 19, 2023 6:14pm EDT', ' March 19, 2023 12:02am EDT', ' March 18, 2023 11:44pm EDT', ' March 7, 2023 10:11am EST', ' February 21, 2023 12:35pm EST', ' February 14, 2023 4:05pm EST', ' February 13, 2023 7:23am EST', '\\n        March 19, 2023 9:00pm EDT\\n      ', '\\n        March 19, 2023 8:28pm EDT\\n      ', '\\n        March 19, 2023 7:50pm EDT\\n      ', '\\n        March 19, 2023 1:12pm EDT\\n      ', ' March 19, 2023 1:05pm EDT', ' March 19, 2023 10:51am EDT', ' March 17, 2023 2:48pm EDT', ' March 17, 2023 1:15pm EDT', 'March 20, 2023', 'March 19, 2023', 'March 19, 2023', 'March 19, 2023', ' March 19, 2023 9:00am EDT', ' March 19, 2023 8:00am EDT', ' March 18, 2023 9:59pm EDT', ' March 18, 2023 2:00pm EDT']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# STEP 8: Convert everything into pandas DataFrame"
      ],
      "metadata": {
        "id": "vYDHwbyzbIW0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "news_data = pd.DataFrame({\n",
        "    \"Title\": news_titles,\n",
        "    \"Link\": urls,\n",
        "    \"Corpus\": art_text,\n",
        "    \"category\": news_cat,\n",
        "    \"author\": author_names,\n",
        "    \"timeanddate\": time,\n",
        "})\n",
        "news_data.dropna(how='any', inplace=True)"
      ],
      "metadata": {
        "id": "W1BgaPxf-GJj"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# STEP 9: Sentimental Analysis\n",
        "### download nltk dependencies"
      ],
      "metadata": {
        "id": "-GaJHFxPdfD3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "\n",
        "# Download stop words\n",
        "nltk.download('stopwords')"
      ],
      "metadata": {
        "id": "NPocVa4bR313",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "71cd4967-442f-4a97-f9fe-c387155196e6"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('punkt')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KSSHIT1EbQEP",
        "outputId": "f78fa6f0-eed8-4335-d83b-5fe34a7ebed5"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 9.1 Remove Stopwords and uneccessary words from the corpus"
      ],
      "metadata": {
        "id": "5Xx4CsNvNyMM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# setting up stopwords with some additional stop words\n",
        "stop_words = set(stopwords.words('english'))\n",
        "stop_words.update(['close', 2, ':', 3, 'video', 'fox news', 'flash', 'top', 'headlines', \".\", \",\", \"news\", \"fox\",\"?\", \";\", \"(\", \")\", \"-\", \"!\", \"@\", \"#\",\"$\",\"%\",\"^\",\"&\",\"*\", \"also\", \"march\", \"get\", \"one\", \"``\", \"'\", \"said\", \"new\", \"two\", \"'s\", 'would', \"n't\", 'first', '~', '’', '‘', 'told', 'images','according', 'years', 'like'])\n",
        "\n",
        "# Define function to remove stop words\n",
        "def remove_stopwords(text):\n",
        "    if text != None:\n",
        "      tokens = word_tokenize(text.lower())\n",
        "      filtered_tokens = [word for word in tokens if word not in stop_words]\n",
        "      return ' '.join(filtered_tokens)\n",
        "\n",
        "# Apply function to 'corpus' column\n",
        "news_data['Corpus'] = news_data['Corpus'].apply(remove_stopwords)"
      ],
      "metadata": {
        "id": "fd7PlqBcawSn"
      },
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### See the new Corpus"
      ],
      "metadata": {
        "id": "c1-tMY6lN12y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "news_data[\"Corpus\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VAk4sF0QdnCf",
        "outputId": "8d9cdcc6-cf45-4f94-8360-4177db1435da"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      watch security footage appears show mexican mi...\n",
              "1      north korean defectors honor otto warmbier kim...\n",
              "2      nfl uncharted waters following damar hamlin co...\n",
              "3      objective parent knows children belong drag sh...\n",
              "4      kilmeade biden admin perfects blame game host ...\n",
              "                             ...                        \n",
              "144    good samaritan helped stop gym attacker good s...\n",
              "149    15 check clicking foxnews.com.a 208 million mu...\n",
              "150    15 check clicking foxnews.com.as smallest grou...\n",
              "151    turkey earthquake captured dashcam dashcam cap...\n",
              "152    15 check clicking foxnews.com.in front empty d...\n",
              "Name: Corpus, Length: 142, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 9.2 Sentiment Analysis\n",
        "### Positive Negative or Neutral\n",
        "### Doing sentiment analysis of news corpus and keeping the analysis report in a new column ***news_data['sentiment']***"
      ],
      "metadata": {
        "id": "NL2nvsWLfpcv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from textblob import TextBlob\n",
        "def get_sentiment(text):\n",
        "    blob = TextBlob(text)\n",
        "    sentiment_score = blob.sentiment.polarity\n",
        "    if sentiment_score > 0:\n",
        "        return 'positive'\n",
        "    elif sentiment_score < 0:\n",
        "        return 'negative'\n",
        "    else:\n",
        "        return 'neutral'\n",
        "news_data['sentiment'] = news_data['Corpus'].apply(get_sentiment)"
      ],
      "metadata": {
        "id": "SCJZBjlZfrlR"
      },
      "execution_count": 136,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Visualising dataframe"
      ],
      "metadata": {
        "id": "AIIkJtIIyrAi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "news_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "DXk7FPkTgNN3",
        "outputId": "2e333651-1308-4256-805f-1b39b92e0ceb"
      },
      "execution_count": 137,
      "outputs": [
        {
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              "                                               Title  \\\n",
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              "2   March 19, 2023 6:16pm EDT  positive  \n",
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    {
      "cell_type": "code",
      "source": [
        "news_data.tail()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "id": "zmu8FulT9mYC",
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      "execution_count": 138,
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        {
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            "text/plain": [
              "                                                 Title  \\\n",
              "144  Hatchet-wielding attacker subdued during wild ...   \n",
              "149  Grand Canyon National Park planning for a $208...   \n",
              "150  Only big whales can lunge-feed successfully, a...   \n",
              "151  Strong earthquake kills at least 14 in Ecuador...   \n",
              "152  London partygoers rave to AI-generated beats i...   \n",
              "\n",
              "                                                  Link  \\\n",
              "144  https://www.foxnews.com/media/hatchet-wielding...   \n",
              "149  https://www.foxnews.com/us/grand-canyon-nation...   \n",
              "150  https://www.foxnews.com/science/only-big-whale...   \n",
              "151  https://www.foxnews.com/world/strong-earthquak...   \n",
              "152  https://www.foxnews.com/entertainment/london-p...   \n",
              "\n",
              "                                                Corpus       category author  \\\n",
              "144  good samaritan helped stop gym attacker good s...          media          \n",
              "149  15 check clicking foxnews.com.a 208 million mu...             us          \n",
              "150  15 check clicking foxnews.com.as smallest grou...        science          \n",
              "151  turkey earthquake captured dashcam dashcam cap...          world          \n",
              "152  15 check clicking foxnews.com.in front empty d...  entertainment          \n",
              "\n",
              "                    timeanddate sentiment  \n",
              "144   March 17, 2023 1:15pm EDT  positive  \n",
              "149   March 19, 2023 9:00am EDT  positive  \n",
              "150   March 19, 2023 8:00am EDT  positive  \n",
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              "152   March 18, 2023 2:00pm EDT  positive  "
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              "      <td>Strong earthquake kills at least 14 in Ecuador...</td>\n",
              "      <td>https://www.foxnews.com/world/strong-earthquak...</td>\n",
              "      <td>turkey earthquake captured dashcam dashcam cap...</td>\n",
              "      <td>world</td>\n",
              "      <td></td>\n",
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              "      <td>London partygoers rave to AI-generated beats i...</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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    },
    {
      "cell_type": "markdown",
      "source": [
        "# STEP 10: Visualising news volume w.r.t categories\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "## Visualize on the basis of news category. For eg: Politics, Science"
      ],
      "metadata": {
        "id": "2HAXh-UbbSKr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "category_counts = news_data['category'].value_counts()\n",
        "print(category_counts)"
      ],
      "metadata": {
        "id": "hE559_jPRdnd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f9c1e35c-7d72-4357-ef17-80121941fa4d"
      },
      "execution_count": 219,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "us                  27\n",
            "entertainment       16\n",
            "politics            14\n",
            "media               12\n",
            "world               12\n",
            "lifestyle           11\n",
            "opinion             10\n",
            "sports               9\n",
            "health               8\n",
            "food-drink           4\n",
            "tech                 4\n",
            "personal-finance     3\n",
            "weather-news         2\n",
            "economy              2\n",
            "markets              2\n",
            "technology           1\n",
            "auto                 1\n",
            "retail               1\n",
            "learn                1\n",
            "extreme-weather      1\n",
            "science              1\n",
            "Name: category, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Plotting the news volume with respect to its category"
      ],
      "metadata": {
        "id": "yzYl_JNLyBAV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "category_counts.plot(kind='bar')\n",
        "plt.title(\"No. of News category in foxnews recently\")\n",
        "plt.xlabel(\"category\")\n",
        "plt.ylabel(\"number of news\")\n",
        "desc = f'Chart Analysis \\nThe chart below depicts the information of number of news\\n published recently with respect to its category. Here we can \\nsee that there are maximum news related to \"{category_counts[category_counts == max(category_counts.values)].index[0]}\". There are \\n{max(category_counts.values)} news on this topic.'\n",
        "plt.text(0,35, desc, ha='left', va='baseline', fontsize = \"large\")\n",
        "plt.subplots_adjust(bottom=0)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "_Hanb4DnRyUd",
        "outputId": "22c63efa-17e7-4506-cd2a-9b2a87e864cf"
      },
      "execution_count": 232,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Sentiment Analysis (Visualization)\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "OwytEJ8QFCgy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sentiment_counts = news_data['sentiment'].value_counts()\n",
        "print(type(sentiment_counts))"
      ],
      "metadata": {
        "id": "ulXaQPiYbMgQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "71e007b8-9db4-46ad-fe88-9646c189dd08"
      },
      "execution_count": 180,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.series.Series'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Pie Chart Analysis\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "### The pie chart below shows that more of **positive** news have been published than **negative** news recently in *foxnews.com*."
      ],
      "metadata": {
        "id": "d7ALDjp2OS38"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.pie(sentiment_counts.values, labels=sentiment_counts.index, autopct='%1.1f%%')\n",
        "plt.title(\"Percentage of positive and negative news published recently\")\n",
        "desc = f'Chart Analysis \\nThe pie Chart show the percentage of Positive and Negative sentimented \\nnews published in Foxnews.com recently. This analysis says that the news \\nhave {max(sentiment_counts.values)} of {sentiment_counts[sentiment_counts == max(sentiment_counts.values)].index[0]} news. This means more {sentiment_counts[sentiment_counts == max(sentiment_counts.values)].index[0]} news has been published \\nrecently in foxnews.com. This means it is {\"good\" if sentiment_counts[sentiment_counts == max(sentiment_counts.values)].index[0] == \"positive\" else \"bad\"}.'\n",
        "plt.text(-1.5,1.5, desc, ha='left', va='baseline', fontsize = \"large\")\n",
        "plt.subplots_adjust(bottom=-0.2)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "tO-5u5UebYS_",
        "outputId": "dfa3c43e-2f49-4698-f709-c0875e060b9a"
      },
      "execution_count": 214,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Sentiment + news category Volume Relation\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "### Lets analyse which category is likely to have more positive or negative news recently"
      ],
      "metadata": {
        "id": "rAckPsoRMwds"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# grouping the data w.r.t category and sentiment and resetting the index\n",
        "category_counts = news_data.groupby(['category', 'sentiment']).size().reset_index(name = 'count')\n",
        "print(category_counts)"
      ],
      "metadata": {
        "id": "IBlExLda-Voc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# pivoting the table of category_counts and making category and index and sentiment as column values\n",
        "'''\n",
        "for eg.\n",
        "Sentiments    |   Positive   |  Negative   |\n",
        "Category      |              |             |\n",
        "--------------------------------------------\n",
        "US            |      20      |    7        |\n",
        "Entertainment |      16      |    Nan      |\n",
        "......        | ............ |............ |\n",
        "'''\n",
        "\n",
        "pivot_table = category_counts.pivot(index='category', columns='sentiment', values='count')"
      ],
      "metadata": {
        "id": "KdEjAfGuFkli"
      },
      "execution_count": 148,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Plotting the chart that shows the sentiments of categorical news"
      ],
      "metadata": {
        "id": "AEIbmCXZwAfW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pivot_table.plot(kind='bar', stacked=True)\n",
        "\n",
        "plt.xlabel('Category')\n",
        "plt.ylabel('Sentiment Count')\n",
        "plt.title('Sentiment Analysis by Category')\n",
        "desc = f'Chart Analysis \\nThe chart below shows the negative and positive sentimental news \\nwith respect to the news categories. Here we can see that which \\ncategory of news consists of what volume of positive or nergative sentiments.'\n",
        "plt.text(-1.5,35, desc, ha='left', va='baseline', fontsize = \"large\")\n",
        "plt.subplots_adjust(bottom=-0.2)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "id": "IbbT8mfbHFbZ",
        "outputId": "7630409a-6311-421f-d454-16d4fb5dff55"
      },
      "execution_count": 212,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Finding most repeated words\n",
        "#### Import dependencies for frequency"
      ],
      "metadata": {
        "id": "XgPMDyQv63gx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.probability import FreqDist"
      ],
      "metadata": {
        "id": "bDQgMB4g69M-"
      },
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Merging the Corpus of all news and its title into one string, tokenizing and using FreqDist to find the most used words in the news publishes recently"
      ],
      "metadata": {
        "id": "l-ctcYvuwISE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "corpus = \" \".join(news_data['Title']) + \" \".join(news_data['Corpus'])\n",
        "tokenized_words = word_tokenize(corpus)\n",
        "frequency = FreqDist(tokenized_words)\n",
        "sorted_frequency = sorted(frequency.items(), key=lambda x: x[1], reverse=True)\n",
        "top_words = [word[0] for word in sorted_frequency[:10]]"
      ],
      "metadata": {
        "id": "IGAGWj2g7Itw"
      },
      "execution_count": 150,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Getting the frequency corresponding to *top_words*."
      ],
      "metadata": {
        "id": "NvFkw7H1xxpo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "top_freq = [word[1] for word in sorted_frequency[:10]]"
      ],
      "metadata": {
        "id": "zhwX1d5T8L_N"
      },
      "execution_count": 153,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Plotting the chart of Most Frequent Words used in News Corpus"
      ],
      "metadata": {
        "id": "It7kwMkJxi_h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 5))\n",
        "colors = plt.cm.tab10(range(len(top_words)))\n",
        "plt.bar(top_words, top_freq, color = colors)\n",
        "plt.title('Top 10 Most Frequent Words in News Corpus')\n",
        "plt.xlabel('Words')\n",
        "plt.ylabel('Frequency')\n",
        "desc = f'Chart Analysis \\nThis bar chart shows the top 10 most frequent words in a news corpus\\ncompiled from Fox News articles. The words are sorted by frequency\\nin descending order, and each bar is colored differently for better\\nvisibility. This Bar graph below shows that \"{top_words[0]}\" is the most \\nrepeated word with the frequency of {top_freq[0]}'\n",
        "plt.text(0, max(top_freq) + 50, desc, ha='left', va='center', fontsize = \"large\")\n",
        "plt.subplots_adjust(bottom=-0.2)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "id": "uT9sBp5j_krR",
        "outputId": "7d00e2bb-dec6-4bc9-ad86-0b8f460ff232"
      },
      "execution_count": 213,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Saving the DataFrame into a CSV File"
      ],
      "metadata": {
        "id": "wBWe6FNAxojs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "news_data.to_csv('fox_news.csv', index = 'False')"
      ],
      "metadata": {
        "id": "DCPIqDefC3-U"
      },
      "execution_count": 93,
      "outputs": []
    }
  ]
}