Stanza_Stanford_ABSA.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "XtDdx4zaaN0B",
        "outputId": "f8030a71-898b-4d8b-ed9d-8e7c18559821"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "('64bit', '')\n",
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            "Collecting qtpy>=2.0.1\n",
            "  Downloading QtPy-2.2.1-py3-none-any.whl (82 kB)\n",
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            "Building wheels for collected packages: jupyternotify\n",
            "  Building wheel for jupyternotify (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for jupyternotify: filename=jupyternotify-0.1.15-py3-none-any.whl size=8741 sha256=573a0a21776943b22f444c3041568076288449a35968c92d1113cfd7f77dbecd\n",
            "  Stored in directory: /root/.cache/pip/wheels/18/31/59/466711d8f86811d1b57f55f114d0516419f0093435f41c65d9\n",
            "Successfully built jupyternotify\n",
            "Installing collected packages: jedi, qtpy, qtconsole, jupyter, jupyternotify\n",
            "Successfully installed jedi-0.18.1 jupyter-1.0.0 jupyternotify-0.1.15 qtconsole-5.3.2 qtpy-2.2.1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "if (!(\"Notification\" in window)) {\n",
              "    alert(\"This browser does not support desktop notifications, so the %%notify magic will not work.\");\n",
              "} else if (Notification.permission !== 'granted' && Notification.permission !== 'denied') {\n",
              "    Notification.requestPermission(function (permission) {\n",
              "        if(!('permission' in Notification)) {\n",
              "            Notification.permission = permission;\n",
              "        }\n",
              "    })\n",
              "}\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting email-notify-magic\n",
            "  Downloading email_notify_magic-0.0.2-py3-none-any.whl (3.5 kB)\n",
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            "Installing collected packages: email-notify-magic\n",
            "Successfully installed email-notify-magic-0.0.2\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import itertools\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "pd.set_option('display.max_columns', None)\n",
        "pd.set_option('display.max_rows', None)\n",
        "\n",
        "class style :\n",
        "   PURPLE = '\\033[95m'\n",
        "   CYAN = '\\033[96m'\n",
        "   DARKCYAN = '\\033[36m'\n",
        "   BLUE = '\\033[94m'\n",
        "   GREEN = '\\033[92m'\n",
        "   YELLOW = '\\033[93m'\n",
        "   RED = '\\033[91m'\n",
        "   BOLD = '\\033[1m'\n",
        "   UNDERLINE = '\\033[4m'\n",
        "   END = '\\033[0m'\n",
        "    \n",
        "from IPython.core.interactiveshell import InteractiveShell\n",
        "InteractiveShell.ast_node_interactivity = \"all\"\n",
        "\n",
        "# check python bit version\n",
        "import platform\n",
        "print(platform.architecture())\n",
        "\n",
        "!pip install jupyternotify\n",
        "import jupyternotify\n",
        "ip = get_ipython()\n",
        "ip.register_magics(jupyternotify.JupyterNotifyMagics)\n",
        "\n",
        "!pip install email-notify-magic\n",
        "%load_ext email_notify_magic"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Install; note that the prefix \"!\" is not needed if you are running in a terminal\n",
        "!pip install stanza\n",
        "\n",
        "# Import the package\n",
        "import stanza\n",
        "nlp = stanza.Pipeline('en')"
      ],
      "metadata": {
        "colab": {
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting stanza\n",
            "  Downloading stanza-1.4.2-py3-none-any.whl (691 kB)\n",
            "\u001b[K     |████████████████████████████████| 691 kB 5.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: torch>=1.3.0 in /usr/local/lib/python3.7/dist-packages (from stanza) (1.12.1+cu113)\n",
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            "Collecting emoji\n",
            "  Downloading emoji-2.1.0.tar.gz (216 kB)\n",
            "\u001b[K     |████████████████████████████████| 216 kB 47.1 MB/s \n",
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            "Building wheels for collected packages: emoji\n",
            "  Building wheel for emoji (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Successfully built emoji\n",
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            "INFO:stanza:Loading these models for language: en (English):\n",
            "============================\n",
            "| Processor    | Package   |\n",
            "----------------------------\n",
            "| tokenize     | combined  |\n",
            "| pos          | combined  |\n",
            "| lemma        | combined  |\n",
            "| depparse     | combined  |\n",
            "| sentiment    | sstplus   |\n",
            "| constituency | wsj       |\n",
            "| ner          | ontonotes |\n",
            "============================\n",
            "\n",
            "INFO:stanza:Use device: cpu\n",
            "INFO:stanza:Loading: tokenize\n",
            "INFO:stanza:Loading: pos\n",
            "INFO:stanza:Loading: lemma\n",
            "INFO:stanza:Loading: depparse\n",
            "INFO:stanza:Loading: sentiment\n",
            "INFO:stanza:Loading: constituency\n",
            "INFO:stanza:Loading: ner\n",
            "INFO:stanza:Done loading processors!\n"
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        "%%notify\n",
        "%%time\n",
        "Review = pd.read_csv(r'resturant.csv',encoding = \"utf-8\")\n",
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            "Wall time: 15.4 ms\n"
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              "                    notification.onclick = function () {\n",
              "                        window.focus();\n",
              "                        this.close();\n",
              "                        };\n",
              "                    } \n",
              "                }     \n",
              "            }\n",
              "        }\n",
              "    }\n",
              ")\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qP9-sQ9BaN0_",
        "outputId": "a2f5e59c-49bb-49c6-a3d2-4fcfc7f656a1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2604, 3)\n"
          ]
        }
      ],
      "source": [
        "\n",
        "Review= Review[['text', 'aspects', 'polarities']]\n",
        "print(Review.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FkQf-4u4aN1K"
      },
      "source": [
        "## Cleaing: Code"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "IQ14etSfaN1q",
        "outputId": "4efc41e2-481c-4c28-bc96-84be5ba8aa94"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 301 ms, sys: 102 ms, total: 403 ms\n",
            "Wall time: 967 ms\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "$(document).ready(\n",
              "    function() {\n",
              "        function appendUniqueDiv(){\n",
              "            // append a div with our uuid so we can check that it's already\n",
              "            // been sent and avoid duplicates on page reload\n",
              "            var notifiedDiv = document.createElement(\"div\")\n",
              "            notifiedDiv.id = \"07560ad0-458e-435b-b78c-ec68f0d34411\"\n",
              "            element.append(notifiedDiv)\n",
              "        }\n",
              "\n",
              "        // only send notifications if the pageload is complete; this will\n",
              "        // help stop extra notifications when a saved notebook is loaded,\n",
              "        // which during testing gives us state \"interactive\", not \"complete\"\n",
              "        if (document.readyState === 'complete') {\n",
              "            // check for the div that signifies that the notification\n",
              "            // was already sent\n",
              "            if (document.getElementById(\"07560ad0-458e-435b-b78c-ec68f0d34411\") === null) {\n",
              "                var notificationPayload = {\"requireInteraction\": false, \"icon\": \"/static/base/images/favicon.ico\", \"body\": \"Cell execution has finished!\"};\n",
              "                if (Notification.permission !== 'denied') {\n",
              "                    if (Notification.permission !== 'granted') { \n",
              "                        Notification.requestPermission(function (permission) {\n",
              "                            if(!('permission' in Notification)) {\n",
              "                                Notification.permission = permission\n",
              "                            }\n",
              "                        })\n",
              "                    }\n",
              "                    if (Notification.permission === 'granted') {\n",
              "                    var notification = new Notification(\"Jupyter Notebook\", notificationPayload)\n",
              "                    appendUniqueDiv()\n",
              "                    notification.onclick = function () {\n",
              "                        window.focus();\n",
              "                        this.close();\n",
              "                        };\n",
              "                    } \n",
              "                }     \n",
              "            }\n",
              "        }\n",
              "    }\n",
              ")\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "%%notify\n",
        "%%time\n",
        "\n",
        "from nltk.corpus import wordnet\n",
        "from nltk import word_tokenize\n",
        "\n",
        "def clean(text):\n",
        "    \n",
        "    text_token = word_tokenize(text)\n",
        "    text_token2 = [x.lower() for x in text_token]\n",
        "    \n",
        "    # Mapping Social-media shortcuts\n",
        "    shortcuts = {'u': 'you','lol': 'laughing out loud', 'k': 'okay', 'm': 'am', 'b4': 'before',\n",
        "                'wlcm': 'welcome', 'bc': 'because', '<3': 'love', 'xoxo': 'love','gr8': 'great', 'bday': 'birthday',\n",
        "                 'awsm': 'awesome', 'gud': 'good', 'h8': 'hate','irl': 'in real life', 'yolo': 'you only live once','btw': 'by the way'}\n",
        "    nsw=pd.Series(text_token)\n",
        "    words=nsw.map(shortcuts).fillna(nsw)\n",
        "    \n",
        "    #Get an integrated list of listed_words (words like yolo must be combined, You Only Live Once :)\n",
        "    words= [word.split(' ') for word in words]    \n",
        "    import itertools\n",
        "    words= list(itertools.chain.from_iterable(words))\n",
        "    \n",
        "    # lemmatize the words with appropriate POS Tag\n",
        "    def pos_tag(word):  \n",
        "        tag= nltk.pos_tag([word])[0][1][0].upper()\n",
        "        dic= {\"J\": wordnet.ADJ,\"N\": wordnet.NOUN,\"V\": wordnet.VERB,\"R\": wordnet.ADV}\n",
        "        return dic.get(tag, wordnet.NOUN)\n",
        "    words = [WordNetLemmatizer().lemmatize(word, pos=pos_tag(word)) for word in words] \n",
        "    words2=[word for word in words if nltk.pos_tag([word])[0][1] not in ['VB','VBG','VBD','VBN','VBP','VBZ','CC','MD','IN']]\n",
        "    \n",
        "    '''# perform stemming for non lemmatized words (like 'cleaning')\n",
        "    from nltk.stem.snowball import SnowballStemmer\n",
        "    \n",
        "    englishStemmer=SnowballStemmer(\"english\")\n",
        "    words=[englishStemmer.stem(word) for  word in words]'''\n",
        "    \n",
        "    '''words=[word.encode('utf-8') for word in words]\n",
        "    words=[hnspl.suggest(word)[0] if hnspl.spell(word)==False else word for word in words]'''\n",
        "    \n",
        "    clean_text=\" \".join(words2)\n",
        "    return clean_text"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QgK4VmXNaN2B"
      },
      "source": [
        "## Stanford&Stanza: ABSA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 695,
          "referenced_widgets": [
            "b56423ecaba24a9fa18caf5c9457542a",
            "f03895a799ba482483423586baf09260",
            "ee46d1331de447a0bf4521ad0d624957",
            "95b6ddfc184446819da6ea9089e7bcde",
            "d09c3bf90aa84648a4c63d574129ced3",
            "bce529a1bcff474c8cf3676d0eda85a3",
            "d402440ba74f4dfbb148adf74939fb80",
            "e622ae9d0bec432e9fc7d3b5d8909d2a",
            "4b2dc08f2ceb48f1addac26f474177b2",
            "49fa0885934440afba4a8894f359dc98",
            "0aaabfc24f5b4066a1f63974bd07733d",
            "536185d02c554507bc1b7edebdec27d4",
            "936e4476b5c840e682d4d59cf4ec5a74",
            "dd5290b94b88416dbd7c36d373a5f3b7",
            "d8d326e0732e44a887199223bcf5e66e",
            "98a952fedd7342ba9d1860049ef94640",
            "2105c269b49d4661978bfb1afb9a8fef",
            "51e2d5dc9a564bc5af7adbe4760c1ebc",
            "105a5b9ccefd4f4d88b3d994661a9626",
            "bb6a6592961a4caf9c249ced7c90cbe9",
            "6ac26640e5a04d22a3ab0d0d1f622fd7",
            "efe75fe3860b4a759bec6d67cd77db0e"
          ]
        },
        "id": "Sv6WCtQEaN2F",
        "outputId": "fb7a3705-7b50-4231-8ad2-3c5651f7d2ce"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading https://raw.githubusercontent.com/stanfordnlp/stanza-resources/main/resources_1.4.1.json:   0%|   …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b56423ecaba24a9fa18caf5c9457542a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:stanza:Downloading default packages for language: en (English) ...\n",
            "INFO:stanza:File exists: /root/stanza_resources/en/default.zip\n",
            "INFO:stanza:Finished downloading models and saved to /root/stanza_resources.\n",
            "INFO:stanza:Checking for updates to resources.json in case models have been updated.  Note: this behavior can be turned off with download_method=None or download_method=DownloadMethod.REUSE_RESOURCES\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading https://raw.githubusercontent.com/stanfordnlp/stanza-resources/main/resources_1.4.1.json:   0%|   …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "536185d02c554507bc1b7edebdec27d4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:stanza:Loading these models for language: en (English):\n",
            "============================\n",
            "| Processor    | Package   |\n",
            "----------------------------\n",
            "| tokenize     | combined  |\n",
            "| pos          | combined  |\n",
            "| lemma        | combined  |\n",
            "| depparse     | combined  |\n",
            "| sentiment    | sstplus   |\n",
            "| constituency | wsj       |\n",
            "| ner          | ontonotes |\n",
            "============================\n",
            "\n",
            "INFO:stanza:Use device: cpu\n",
            "INFO:stanza:Loading: tokenize\n",
            "INFO:stanza:Loading: pos\n",
            "INFO:stanza:Loading: lemma\n",
            "INFO:stanza:Loading: depparse\n",
            "INFO:stanza:Loading: sentiment\n",
            "INFO:stanza:Loading: constituency\n",
            "INFO:stanza:Loading: ner\n",
            "INFO:stanza:Done loading processors!\n"
          ]
        }
      ],
      "source": [
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.corpus import wordnet\n",
        "from nltk.tokenize import word_tokenize, sent_tokenize \n",
        "\n",
        "nltk.download('stopwords')\n",
        "nltk.download('punkt')\n",
        "#nltk.download('averaged_perceptron_tagger')\n",
        "stop_words = set(stopwords.words('english'))\n",
        "stanza.download(\"en\")\n",
        "nlp = stanza.Pipeline('en') # initialize English neural pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496,
          "referenced_widgets": [
            "f68ae85f403e4d08afdacaaf57435dcb",
            "5f494f5d6e4f4c56970165d33fa10e76",
            "14bfaeca96cf462e8b8e757e96ede001",
            "32a79b10ed72447c9dc751a4a2513945",
            "205d9495cd4c4509b7c08bd4224b8858",
            "07ec05930fc7428884e782a18493156b",
            "2ea254882dbb4ecb8ab6225885a9f840",
            "4aaa921606814d2c860830653224052d",
            "c7cc39d1ebc94284949892864f4c5889",
            "54704c2fa94848d89a7e909d83cfd81f",
            "002c8257bf834954a731933842704084"
          ]
        },
        "id": "qJzZZupMaN2K",
        "outputId": "f39feba1-dee0-4f52-843d-ae6b0f932736"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:stanza:Checking for updates to resources.json in case models have been updated.  Note: this behavior can be turned off with download_method=None or download_method=DownloadMethod.REUSE_RESOURCES\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading https://raw.githubusercontent.com/stanfordnlp/stanza-resources/main/resources_1.4.1.json:   0%|   …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f68ae85f403e4d08afdacaaf57435dcb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:stanza:Loading these models for language: en (English):\n",
            "============================\n",
            "| Processor    | Package   |\n",
            "----------------------------\n",
            "| tokenize     | combined  |\n",
            "| pos          | combined  |\n",
            "| lemma        | combined  |\n",
            "| depparse     | combined  |\n",
            "| sentiment    | sstplus   |\n",
            "| constituency | wsj       |\n",
            "| ner          | ontonotes |\n",
            "============================\n",
            "\n",
            "INFO:stanza:Use device: cpu\n",
            "INFO:stanza:Loading: tokenize\n",
            "INFO:stanza:Loading: pos\n",
            "INFO:stanza:Loading: lemma\n",
            "INFO:stanza:Loading: depparse\n",
            "INFO:stanza:Loading: sentiment\n",
            "INFO:stanza:Loading: constituency\n",
            "INFO:stanza:Loading: ner\n",
            "INFO:stanza:Done loading processors!\n"
          ]
        }
      ],
      "source": [
        "import stanza\n",
        "nlp = stanza.Pipeline('en') # initialize pipeline\n",
        "def ABSA(txt):\n",
        "    \n",
        "    txt = txt.lower() # LowerCasing the given Text\n",
        "    sentList = nltk.sent_tokenize(txt) # Splitting the text into sentences\n",
        "\n",
        "    fcluster = []\n",
        "    totalfeatureList = []\n",
        "    finalcluster = []\n",
        "    dic = {}\n",
        "\n",
        "    for line in sentList:\n",
        "        newtaggedList = []\n",
        "        txt_list = nltk.word_tokenize(line) # Splitting up into words\n",
        "        taggedList = nltk.pos_tag(txt_list) # Doing Part-of-Speech Tagging to each word\n",
        "        #taggedList = pos_tagger.tag(txt_list)\n",
        "        newwordList = []\n",
        "        flag = 0\n",
        "        for i in range(0,len(taggedList)-1):\n",
        "            if(taggedList[i][1]==\"NN\" and taggedList[i+1][1]==\"NN\"): # If two consecutive words are Nouns then they are joined together\n",
        "                newwordList.append(taggedList[i][0]+taggedList[i+1][0])\n",
        "                flag=1\n",
        "            else:\n",
        "                if(flag==1):\n",
        "                    flag=0\n",
        "                    continue\n",
        "                newwordList.append(taggedList[i][0])\n",
        "                if(i==len(taggedList)-2):\n",
        "                    newwordList.append(taggedList[i+1][0])\n",
        "\n",
        "        finaltxt = ' '.join(word for word in newwordList) \n",
        "        new_txt_list = nltk.word_tokenize(finaltxt)\n",
        "        #wordsList = [w for w in new_txt_list if not w in stop_words]\n",
        "        taggedList = nltk.pos_tag(new_txt_list)\n",
        "\n",
        "        doc = nlp(finaltxt) # Object of Stanza NLP Pipeleine\n",
        "        \n",
        "        # Getting the dependency relations betwwen the words\n",
        "        dep_node = []\n",
        "        for dep_edge in doc.sentences[0].dependencies:\n",
        "            dep_node.append([dep_edge[2].text, dep_edge[0].id, dep_edge[1]])\n",
        "\n",
        "        # Coverting it into appropriate format\n",
        "        for i in range(0, len(dep_node)):\n",
        "            if (int(dep_node[i][1]) != 0):\n",
        "                dep_node[i][1] = newwordList[(int(dep_node[i][1]) - 1)]\n",
        "\n",
        "        featureList = []\n",
        "        categories = []\n",
        "        for i in taggedList:\n",
        "            if(i[1]=='JJ' or i[1]=='NN' or i[1]=='JJR' or i[1]=='NNS' or i[1]=='RB'):\n",
        "                featureList.append(list(i)) # For features for each sentence\n",
        "                totalfeatureList.append(list(i)) # Stores the features of all the sentences in the text\n",
        "                categories.append(i[0])\n",
        "\n",
        "        for i in featureList:\n",
        "            filist = []\n",
        "            for j in dep_node:\n",
        "                if((j[0]==i[0] or j[1]==i[0]) and (j[2] in [\"nsubj\", \"acl:relcl\", \"obj\", \"dobj\", \"agent\", \"advmod\", \"amod\", \"neg\", \"prep_of\", \"acomp\", \"xcomp\", \"compound\"])):\n",
        "                    if(j[0]==i[0]):\n",
        "                        filist.append(j[1])\n",
        "                    else:\n",
        "                        filist.append(j[0])\n",
        "            fcluster.append([i[0], filist])\n",
        "            \n",
        "    for i in totalfeatureList:\n",
        "        dic[i[0]] = i[1]\n",
        "    \n",
        "    for i in fcluster:\n",
        "        if(dic[i[0]]==\"NN\"):\n",
        "            finalcluster.append(i)\n",
        "        \n",
        "    return(finalcluster)\n",
        "import nltk\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0_Ik-eItaN2Y"
      },
      "source": [
        "## Test "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LAAxDEghaN2d",
        "outputId": "79bf8f2f-e0b7-4823-842d-7af51efe1c39"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['service', ['fast']]]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "txt = \"Service is fast and friendly.\"\n",
        "ABSA(txt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-oJO16w_aN2t",
        "outputId": "cee74587-2b0a-4cb5-9a41-554971e53ea7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['i', ['understand']], ['hour', ['less']], ['check', ['brought']]]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "txt1 = \"Had we been stalling I could understand where they were coming from, but we had been there less than an hour and they hadn't even brought us a check yet!\"\n",
        "\n",
        "ABSA(txt1)"
      ]
    }
  ],
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    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
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        "name": "ipython",
        "version": 3
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      "file_extension": ".py",
      "mimetype": "text/x-python",
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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