Exploring_AmericanStories.ipynb

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      "source": [
        "# Preamble\n",
        "\n",
        "Welcome to American Stories! Please run the next two cells (hint: hold Shift and press Enter three times) to install the required packages, and then we move to working with the American Stories dataset."
      ]
    },
    {
      "cell_type": "code",
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      "outputs": [],
      "source": [
        "###Installs\n",
        "!pip install datasets\n",
        "!pip install ipympl"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FhSpl18kpXNu"
      },
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      "source": [
        "#Imports\n",
        "import json\n",
        "\n",
        "from datasets import load_dataset\n",
        "import tqdm as tq"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6I08ppmNDjO2"
      },
      "source": [
        "# Introduction\n",
        "\n",
        "Welcome to the intro notebook to AmericanStories. This is an expanded version of the intro notebook available here: https://colab.research.google.com/drive/1ifzTDNDtfrrTy-i7uaq3CALwIWa7GB9A?ts=648b98bf.\n",
        "\n",
        "In this notebook, we will do three things:\n",
        "\n",
        "1. Discuss ways to import the data\n",
        "2. Explore the way the data is structured, and show simple code snippets for accessing the data\n",
        "3. Provide three applications that would not be possible with keyword search based methods:\n",
        "\n",
        "A. We show how to find out whether an *article* mentions two terms simultaneously\n",
        "\n",
        "B. We show how to find out what other articles on a page with a keyword hit\n",
        "discuss\n",
        "\n",
        "C. We showcase a wire-cluster pipeline. Wire-clusters are same/similar articles\n",
        "that originate in a article that went out over the newswire. With this you\n",
        "can measure which places were exposed to the same newpaper content\n"
      ]
    },
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      "cell_type": "markdown",
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        "id": "TiE_Y8_YDqUS"
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      "source": [
        "# Part I: importing data\n",
        "\n",
        "There are four main ways of accessing the American Stories data. A key contribution of our project is to make *articles* available, which requires linking together disjoint content regions. To take full advantange of this, we provide access to the data at the article level, or at the level of a scan, that is, one page of one edition of a single newspaper. For both ways, you can either query by year, or get a dump of all data. In sum, the four ways of interacting with our data:\n",
        "\n",
        "a. Article level, for selected years\n",
        "\n",
        "b. Article level, for all years\n",
        "\n",
        "c. Scan level, for selected years\n",
        "\n",
        "d. Scan level, for all years"
      ]
    },
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      "cell_type": "markdown",
      "metadata": {
        "id": "NdAbXSimptTy"
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      "source": [
        "### 1a: Article, level, for selected years\n",
        "\n",
        "We will now select all articles for the year 1900. Below we will explore the resulting dataset in detail"
      ]
    },
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      "source": [
        "# let's start with deciding which years we want data for\n",
        "article_level_desired_years = [\"1900\", ]\n",
        "\n",
        "# now let's load our data, we have to specify the huggingface location of our\n",
        "# data, the fact that we want to have a subset of years, and our desired years\n",
        "dataset_article_level=load_dataset(\"dell-research-harvard/AmericanStories\",\n",
        "                                   \"subset_years\",\n",
        "                                   year_list=article_level_desired_years\n",
        "                                   )"
      ]
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      "cell_type": "markdown",
      "metadata": {
        "id": "1Irgyei2GUXR"
      },
      "source": [
        "### 1b: article level, for all years\n",
        "The next cell contains to get all articles for all years. It is commented because running it requires a lot of bandwidth and data. Please uncomment, and then run to get all data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HUf7tJQ8Gmre"
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      "outputs": [],
      "source": [
        "# Uncomment the next line and execute this cell to download the entire dataset\n",
        "#dataset_article_level_all_years = load_dataset(\"dell-research-harvard/AmericanStories\", \"all_years\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D5yr4cN-IE7j"
      },
      "source": [
        "### 1c: scan level, for selected years\n",
        "The next cell contains to get all scans for selected years."
      ]
    },
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      "source": [
        "# let's start with deciding which years we want data for\n",
        "scan_level_desired_years = [\"1900\",]\n",
        "\n",
        "# now let's load our data, we have to specify the huggingface location of our\n",
        "# data, the fact that we want to have a subset of years, and our desired years\n",
        "dataset_scan_level=load_dataset(\"dell-research-harvard/AmericanStories\",\n",
        "                                \"subset_years_content_regions\",\n",
        "                                year_list=scan_level_desired_years\n",
        "                                )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a6mpQd-CIzmQ"
      },
      "source": [
        "### 1d: scan level, for all years\n",
        "The next cell contains to get all scans for all years. It is commented because running it requires a lot of bandwidth and data. Please uncomment, and then run to get all data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D3m1XvKXIzmQ"
      },
      "outputs": [],
      "source": [
        "# Uncomment the next line and execute this cell to download the entire dataset\n",
        "#dataset_scan_level_all_years = load_dataset(\"dell-research-harvard/AmericanStories\", \"all_years\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RtgijsI_JFeQ"
      },
      "source": [
        "# Part II: Exploring the data, with applications\n",
        "\n",
        "We will do three things in the next section:\n",
        "\n",
        "1. Discuss the structure of the returned dataset\n",
        "2. Discuss how to access the data\n",
        "3. Implement our applications"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f_tCqR68J51Y"
      },
      "source": [
        "### IIa: Article level"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cvNb2plPJCPF",
        "outputId": "8d0824d1-3263-4023-bc06-1388d58db7db"
      },
      "outputs": [],
      "source": [
        "# let's inspect the structure\n",
        "print(dataset_article_level)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "umVyAxpUKnF-"
      },
      "outputs": [],
      "source": [
        "# We see that we have 1.1mln articles for 1900, and that we have several self-explanatory features for each article. Let's explore the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OjfDo2Z12M9Y",
        "outputId": "edcece3f-9241-4de3-b383-4382ef4f5809"
      },
      "outputs": [],
      "source": [
        "# the structure of the output is a dictionary. The keys of the dictionary are the fields we have for each article\n",
        "dataset_article_level[\"1900\"].features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hAKRRyJTKyg-",
        "outputId": "02e12b12-f423-4caa-d08e-71745fe7cd8c"
      },
      "outputs": [],
      "source": [
        "# let's inspect a random article from 1900\n",
        "dataset_article_level[\"1900\"][5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-vHoN1lw13mC",
        "outputId": "fae9c407-a3c7-47b7-c189-956753863763"
      },
      "outputs": [],
      "source": [
        "# we can see the headline, and that this appeared in the Evening star. Below we will learn more about the Evening star. For now, let's inspect the article\n",
        "print(dataset_article_level[\"1900\"][5][\"article\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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      "outputs": [],
      "source": [
        "# it is easy to loop through articles and see if it mentions 'spring style'\n",
        "# here we will loop through the first ten articles\n",
        "\n",
        "# let's initialize a dict for our articles. Let's collect the index of the\n",
        "# article that contains our desired string as well as the article_id in case\n",
        "# we want to find articles back later\n",
        "dict_of_articles_containing_spring_style = {}\n",
        "\n",
        "# we are interesting in spring style\n",
        "str_of_interest = \"spring style\"\n",
        "\n",
        "# let's loop!\n",
        "for article_n in range(10):\n",
        "\n",
        "  # let's grab the article data\n",
        "  article = dataset_article_level[\"1900\"][article_n]\n",
        "\n",
        "  # let's grab the article text\n",
        "  article_text = article[\"article\"]\n",
        "\n",
        "  # check if we see the text\n",
        "  if str_of_interest in article_text:\n",
        "    dict_of_articles_containing_spring_style[article_n] = article[\"article_id\"]\n",
        "  else:\n",
        "    pass\n",
        "\n",
        "# let's see which articles feature this text\n",
        "print(dict_of_articles_containing_spring_style)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SnFLxE5DNbbE"
      },
      "source": [
        "## Application: Two strings within the *same article*\n",
        "\n",
        "Previous methods for querying historical newspapers often relied on keyword\n",
        "searches.\n",
        "\n",
        "This made it impossible to verify if the same *article* mentioned\n",
        "two terms.\n",
        "\n",
        "Our dataset allows this, let's work through a simple example"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AZ4fGNwSOH9M",
        "outputId": "b9dc39e3-d0fc-4d5d-b9b1-795657c7e078"
      },
      "outputs": [],
      "source": [
        "# let's see if we can see if an article mentions both spring style\n",
        "# and infants shoes\n",
        "str_of_interest_1 = \"spring style\"\n",
        "str_of_interest_2 = \"infants shoes\"\n",
        "\n",
        "# we want to find articles back later\n",
        "dict_of_articles_containing_spring_style_infants_shoes = {}\n",
        "\n",
        "# proceed like before, restricting ourselves to the first 10 articles\n",
        "for article_n in range(10):\n",
        "\n",
        "  # let's grab the article data\n",
        "  article = dataset_article_level[\"1900\"][article_n]\n",
        "\n",
        "  # let's grab the article text. We need to worry about capitalization here\n",
        "  article_text = article[\"article\"].lower()\n",
        "\n",
        "  # we now test for both strings being present\n",
        "  if (\n",
        "      str_of_interest_1 in article_text\n",
        "      and\n",
        "      str_of_interest_2 in article_text\n",
        "  ):\n",
        "    dict_of_articles_containing_spring_style_infants_shoes[article_n] = (\n",
        "        article[\"article_id\"]\n",
        "    )\n",
        "  else:\n",
        "    pass\n",
        "\n",
        "# let's see which articles feature this text\n",
        "print(dict_of_articles_containing_spring_style_infants_shoes)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "maV8wt8HRx7G",
        "outputId": "d3068fd3-92e2-40e5-cd92-0dc39dc2dab5"
      },
      "outputs": [],
      "source": [
        "\n",
        "dataset_article_level\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YWhS51tRQ4Aq",
        "outputId": "93f85d92-7848-46b2-d726-b1e0afa86811"
      },
      "outputs": [],
      "source": [
        "# in our article we found that the Boer War was the biggest story. We can\n",
        "# see which articles mention both the Boer War, and Winston Churchill who\n",
        "# was there as a report. Let's try with all articles this time\n",
        "str_of_interest_1 = \"boer\"\n",
        "str_of_interest_2 = \"churchill\"\n",
        "\n",
        "# we want to find articles back later\n",
        "dict_of_articles_containing_boer_war_churchill = {}\n",
        "\n",
        "# proceed like before, looping through all articles. This should take 3 to 4 min\n",
        "for article_n in tq.tqdm(range(1118970)):\n",
        "\n",
        "  # let's grab the article data\n",
        "  article = dataset_article_level[\"1900\"][article_n]\n",
        "\n",
        "  # let's grab the article text. We need to worry about capitalization here\n",
        "  article_text = article[\"article\"].lower()\n",
        "\n",
        "  # we now test for both strings being present\n",
        "  if (\n",
        "      str_of_interest_1 in article_text\n",
        "      and\n",
        "      str_of_interest_2 in article_text\n",
        "  ):\n",
        "    dict_of_articles_containing_boer_war_churchill[article_n] = (\n",
        "        article[\"article_id\"]\n",
        "    )\n",
        "  else:\n",
        "    pass\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2E0GcDIgS2vJ",
        "outputId": "7f591bb4-faa4-4453-dd95-871ea8b02941"
      },
      "outputs": [],
      "source": [
        "# let's see how many articles we got\n",
        "print(len(dict_of_articles_containing_boer_war_churchill))\n",
        "\n",
        "# let's print the results\n",
        "print(dict_of_articles_containing_boer_war_churchill)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wm1IOa3CS7ad",
        "outputId": "9215531e-d1fb-484a-fbf7-31ae16e0a945"
      },
      "outputs": [],
      "source": [
        "# let's inspect the first one\n",
        "dataset_article_level[\"1900\"][5538]\n",
        "\n",
        "# this article is from the Age Herald from May 12th, and describes the progress of the war"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7JrfDJe6TQvl",
        "outputId": "64013d2b-f5f0-4daf-8da7-914769f70fe6"
      },
      "outputs": [],
      "source": [
        "# let's print the article\n",
        "print(dataset_article_level[\"1900\"][5538][\"article\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FdUdlFfiDbqw"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "# It is possible to visualze the frequency of appearance over time\n",
        "\n",
        "# let's initialize a list of our dates\n",
        "list_of_dates = []\n",
        "\n",
        "# let's loop over our article indices\n",
        "for article_n in dict_of_articles_containing_boer_war_churchill.keys():\n",
        "\n",
        "  # let's grab the date\n",
        "  date = dataset_article_level[\"1900\"][article_n]['date']\n",
        "\n",
        "  # let's add to our list\n",
        "  list_of_dates.append(date)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 857
        },
        "id": "Dr3vHdjnFSym",
        "outputId": "cd23a17c-d7f0-477d-ae64-1275d1491858"
      },
      "outputs": [],
      "source": [
        "#  now let's plot the frequency by date\n",
        "df = pd.DataFrame({'date': list_of_dates})\n",
        "df.groupby('date').size().plot(kind='bar', figsize=(15,9))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2wzo4ioa7aet"
      },
      "source": [
        "### IIb: Scan level"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CZThNVYp7Yjb",
        "outputId": "6c966bbc-e133-4c45-e7a0-f6425a451e5b"
      },
      "outputs": [],
      "source": [
        "# let's inspect the structure\n",
        "print(dataset_scan_level)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Li7QN1wo88Y2",
        "outputId": "cc4001f9-56be-4e33-c48d-72ed27040999"
      },
      "outputs": [],
      "source": [
        "# let's inspect the data for 1900. Note that this data is provided as a string.\n",
        "# We can easily convert to a JSON\n",
        "\n",
        "# get the string\n",
        "raw_data_string = dataset_scan_level[\"1900\"][0]['raw_data_string']\n",
        "\n",
        "# convert to a dict\n",
        "article_json = json.loads(raw_data_string)\n",
        "\n",
        "# inspect which features we have\n",
        "article_json.keys()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0JDyYovhCZhT"
      },
      "source": [
        "Each scan contains several pieces of information:\n",
        "\n",
        "1. The page it appears on\n",
        "2. The URL linking to the original page scan\n",
        "3. Meta-information on the scan (scan and scan_ocr)\n",
        "4. A dictionary of the bounding boxes that we identified on the page\n",
        "5. The date of this edition\n",
        "6. A dictionary of full_articles appearing on this page. Here it is\n",
        "especially important to note that we include a list of the bounding\n",
        "boxes that together form the article\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wle6sE7c-DhU",
        "outputId": "0d13b21e-2c24-477b-ae09-1b30f2e0a773"
      },
      "outputs": [],
      "source": [
        "# Each scan contains several pieces of information, here we are just accessing the\n",
        "# first article\n",
        "article_json['full articles'][0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oCb88C-WU6H7"
      },
      "outputs": [],
      "source": [
        "# note that this dictionary lists the bounding boxes the article is composed\n",
        "# of, the article text, and the article id. If we want, we can use this article\n",
        "# id to link articles we found above to their pages. Note that all article text\n",
        "# that you can explore using the article data, you can also explore using the\n",
        "# scan data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JmUjROjEV71A"
      },
      "source": [
        "### Let's now go back to the evenstar\n",
        "\n",
        "We will try to find the page that the article we identified above appeared on"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UF27d_TXWHvd",
        "outputId": "af4c1b81-c474-4f2c-9966-4cdc96308bcb"
      },
      "outputs": [],
      "source": [
        "# let's start with identifying the article id\n",
        "article_id_of_interest = dict_of_articles_containing_spring_style_infants_shoes[5]\n",
        "print(article_id_of_interest)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MvE2dF9L4fW6",
        "outputId": "e514c998-147a-44d7-d03c-5c6acb7fbe49"
      },
      "outputs": [],
      "source": [
        "# we will take our article and loop through articles on page to find the\n",
        "# relevant page\n",
        "\n",
        "# we want to find articles back later. Let's record the index of the page and\n",
        "# then the index of the article that we're looking for\n",
        "page_with_hit = {}\n",
        "\n",
        "# proceed like before, looping through all scans (we saw above how many there are).\n",
        "# This should take 3\n",
        "for scan_n in tq.tqdm(range(77496)):\n",
        "\n",
        "  # get the string\n",
        "  raw_data_string = dataset_scan_level[\"1900\"][scan_n]['raw_data_string']\n",
        "\n",
        "  # convert to a dict\n",
        "  article_json = json.loads(raw_data_string)\n",
        "\n",
        "  # now we have to loop through articles\n",
        "  for article_n in range(len(article_json[\"full articles\"])):\n",
        "\n",
        "    # let's grab the article\n",
        "    article_data = article_json['full articles'][article_n]\n",
        "\n",
        "    # note that the ids have '.json' attached here\n",
        "    article_id = article_data[\"id\"].split(\".\")[0]\n",
        "\n",
        "    # let's see if the id matches\n",
        "    if article_id == article_id_of_interest:\n",
        "      page_with_hit[scan_n] = article_n\n",
        "    else:\n",
        "      pass"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bd0TUshPXKcp",
        "outputId": "96a9ae46-863c-4ecd-edf2-a69c41609a00"
      },
      "outputs": [],
      "source": [
        "page_with_hit"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ASbbHyOYIKJ"
      },
      "source": [
        "## Application: What other articles are printed with our article\n",
        "\n",
        "Now that we have identified the page we can ask what other articles were\n",
        "printed on the page that our article of interest appears on!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zxaAQubrYWKK"
      },
      "outputs": [],
      "source": [
        "# let's get the page our articles appears on\n",
        "raw_data_string = dataset_scan_level[\"1900\"][1]['raw_data_string']\n",
        "\n",
        "# convert to a dict\n",
        "article_json = json.loads(raw_data_string)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8nf-mvAgYf9E",
        "outputId": "09bdd1dc-96ee-4f90-d510-5d6d0356f7d9"
      },
      "outputs": [],
      "source": [
        "# let's inspect that we have recovered our article\n",
        "article_json['full articles'][5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cRoEyMGlYme5",
        "outputId": "c21f1070-f713-4a62-abb5-795575210776"
      },
      "outputs": [],
      "source": [
        "# now we can inspect what else appeared on that page that day!\n",
        "article_json['full articles']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4IJQC_EIZJjr",
        "outputId": "fa6c51ab-d51a-48d5-cee2-db72cafc92de"
      },
      "outputs": [],
      "source": [
        "# finally, we can inspect the image of the original page!\n",
        "article_json['scan']\n",
        "\n",
        "# The jp2 URL is the URL to the image, let's visualize it, this will take a bit of code"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 836
        },
        "id": "N0Rms_HcaD5W",
        "outputId": "bea565f3-8525-4251-941f-1f144e6c3b21"
      },
      "outputs": [],
      "source": [
        "import requests\n",
        "import cv2\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "def get_ca_scan_img(ca_url):\n",
        "  img_download_session = requests.Session()\n",
        "  response = img_download_session.get(ca_url)\n",
        "  if response.status_code != 200:\n",
        "    print(f'Error! {response.status_code}')\n",
        "    print(f'Please verify that {ca_url} is a valid chronicling america url!')\n",
        "    response.raise_for_status()\n",
        "\n",
        "  data = response.content\n",
        "  ca_img = cv2.imdecode(np.frombuffer(data, np.uint8), cv2.IMREAD_COLOR)\n",
        "  return ca_img\n",
        "\n",
        "# grab the url\n",
        "ca_url = article_json['scan']['jp2_url']\n",
        "\n",
        "#Get the image scan\n",
        "ca_img = get_ca_scan_img(ca_url)\n",
        "\n",
        "# Create a figure for the plot\n",
        "plt.ion()\n",
        "fig, ax = plt.subplots(1)\n",
        "fig.set_size_inches(10, 10)\n",
        "\n",
        "# Display image\n",
        "ax.imshow(ca_img)\n",
        "fig.suptitle('')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OKiyHDu9YqeG"
      },
      "outputs": [],
      "source": [
        "# with a bit more work, this routine can of course be expanded to the edition,\n",
        "# that is, all pages in that newspaper on the same day"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nvxPaXwXYzxV"
      },
      "source": [
        "# Application: Spelling checking\n",
        "\n",
        "There is natural variation in the quality of the scans, and of OCR. It is\n",
        "straightforward to apply a spelling checker to any article. Here we briefly\n",
        "demonstrate how\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oY8rrDUr2BDY"
      },
      "outputs": [],
      "source": [
        "# let's return to our article about shoes\n",
        "shoes_article = dataset_article_level[\"1900\"][5]['article']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IdcenrSGZ63T",
        "outputId": "50900859-ddf5-4095-b3a6-99b0ef607b51"
      },
      "outputs": [],
      "source": [
        "# let's have a look\n",
        "print(shoes_article)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5wgrduk8Z-IC"
      },
      "source": [
        "The article text contains a few mistakes that we can correct. Looking at the text of the article, a few errors are apparent:\n",
        "1. Some spelling mistakes exist, for example \"peopie\" in the first/second line\n",
        "2. Some words are divided between multiple lines (\"peo- pie\", \"rep utable\", \"At tention\", etc).\n",
        "3. Capitalization errors are somewhat common: \"i'll\", \"...lines Of...\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EIPvMQOtaIHP",
        "outputId": "f6046a55-ee18-46e5-c8ee-d2c59eedf07a"
      },
      "outputs": [],
      "source": [
        "# we have to download a spellchecking package\n",
        "!pip install symspellpy\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FiTP4yy1c9oj",
        "outputId": "439ef69d-5d22-4a1e-a9ac-4284e88bcd4f"
      },
      "outputs": [],
      "source": [
        "# let's initialize the package\n",
        "import pkg_resources\n",
        "from symspellpy import SymSpell, Verbosity\n",
        "import string\n",
        "\n",
        "sym_spell = SymSpell(max_dictionary_edit_distance=2, prefix_length=7)\n",
        "en_dict = pkg_resources.resource_filename('symspellpy', 'frequency_dictionary_en_82_765.txt')\n",
        "sym_spell.load_dictionary(en_dict, term_index=0, count_index=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tzFljHkyc-fb"
      },
      "outputs": [],
      "source": [
        "# we now create a few functions that take care of the issues flagged above\n",
        "\n",
        "# thsese two functions implement spelling corrections\n",
        "def check_word(word):\n",
        "  no_punc_word = word.strip(string.punctuation)\n",
        "  if len(no_punc_word) > 0:\n",
        "    suggestions = sym_spell.lookup(no_punc_word, Verbosity.CLOSEST, max_edit_distance=1, include_unknown=True, transfer_casing=True)\n",
        "  else:\n",
        "    return word\n",
        "  return word.replace(no_punc_word, suggestions[0].term)\n",
        "\n",
        "def spell_check(text):\n",
        "  lines = text.split('\\n')\n",
        "  checked_lines = []\n",
        "  for line in lines:\n",
        "    words = line.split(' ')\n",
        "    checked_line = ' '.join([check_word(word) for word in words])\n",
        "    checked_lines.append(checked_line)\n",
        "  return '\\n'.join(checked_lines)\n",
        "\n",
        "# this function checks capitalization\n",
        "def capitalization_check(text):\n",
        "  lines = text.split('\\n')\n",
        "  checked_lines = []\n",
        "  for line in lines:\n",
        "    words = line.split(' ')\n",
        "    for i in range(1, len(words)):\n",
        "      if words[i-1][-1] in ['.', '!', '?']:\n",
        "        words[i] = words[i].capitalize()\n",
        "      else:\n",
        "        no_punc_word = words[i].strip(string.punctuation)\n",
        "        if no_punc_word in sym_spell.words and no_punc_word not in ['i', \"i'll\"]: # Check that the word is not a propper noun\n",
        "          words[i] = words[i].replace(no_punc_word, no_punc_word.lower())\n",
        "\n",
        "    checked_lines.append(' '.join(words))\n",
        "  return '\\n'.join(checked_lines)\n",
        "\n",
        "# this functions corrects line breaks\n",
        "def line_merge(text):\n",
        "  lines = [l.split() for l in text.split('\\n')]\n",
        "  for i in range(len(lines) - 1):\n",
        "    if len(lines[i]) == 0 or len(lines[i+1]) == 0:\n",
        "      continue\n",
        "    elif lines[i][-1][-1] == '-': # Automatically merge if a line ends with a hyphen\n",
        "      lines[i][-1] = lines[i][-1][:-1] + lines[i+1][0]\n",
        "      lines[i+1] = lines[i+1][1:]\n",
        "    elif lines[i][-1].strip(string.punctuation).lower() not in sym_spell.words or lines[i+1][0].strip(string.punctuation).lower() not in sym_spell.words:\n",
        "      if (lines[i][-1].strip(string.punctuation).lower() + lines[i+1][0].strip(string.punctuation).lower()) in sym_spell.words:\n",
        "        lines[i][-1] += lines[i+1][0]\n",
        "        lines[i+1] = lines[i+1][1:]\n",
        "\n",
        "  return '\\n'.join([' '.join(l) for l in lines])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2nbl3M3AdSW9"
      },
      "outputs": [],
      "source": [
        "# this functions implements all three methods\n",
        "def postprocess(text):\n",
        "  merged = line_merge(text)\n",
        "  checked = spell_check(merged)\n",
        "  capitalization_normalized = capitalization_check(checked)\n",
        "  return capitalization_normalized"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sYwrMqtjdTmu",
        "outputId": "09f248c5-6260-48d0-8df3-50f82631ccd7"
      },
      "outputs": [],
      "source": [
        "# now we can simple pass our text to the spelling correction routine!\n",
        "print(postprocess(shoes_article))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oyZ4vQHPnSVo"
      },
      "source": [
        "Postprocessing can significantly help text, but can also create errors. If the dictionary does not include a correctly transcribed proper noun or anachronism, it can \"correct\" it to an erroneous word."
      ]
    }
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