diff --git "a/Week13_\354\230\210\354\212\265\352\263\274\354\240\234_\352\266\214\354\247\200\354\233\220.ipynb" "b/Week13_\354\230\210\354\212\265\352\263\274\354\240\234_\352\266\214\354\247\200\354\233\220.ipynb"
new file mode 100644
index 0000000..769ff66
--- /dev/null
+++ "b/Week13_\354\230\210\354\212\265\352\263\274\354\240\234_\352\266\214\354\247\200\354\233\220.ipynb"
@@ -0,0 +1,496 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##1. LDA 기반 토픽 모델링 (20 뉴스그룹)"
+      ],
+      "metadata": {
+        "id": "C7izBqtjNoCq"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "QdgfVsTfNc7m",
+        "outputId": "a448bba1-f61a-4a1c-d8c7-634a0fba494e"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "CountVectorizer Shape: (7862, 1000)\n",
+            "(8, 1000)\n",
+            "Topic # 0\n",
+            "10 year medical health 1993 20 12 disease cancer team patients research number new 11\n",
+            "Topic # 1\n",
+            "don just like know think good time ve does way really people want ll right\n",
+            "Topic # 2\n",
+            "image file jpeg output program gif images format files color entry use bit 03 02\n",
+            "Topic # 3\n",
+            "armenian armenians turkish people said turkey armenia government genocide turks muslim russian greek azerbaijan killed\n",
+            "Topic # 4\n",
+            "israel jews dos jewish israeli dos dos arab state people arabs palestinian adl ed anti peace\n",
+            "Topic # 5\n",
+            "edu com available graphics ftp window use mail data motif software version pub information server\n",
+            "Topic # 6\n",
+            "god people jesus church believe say christ does christian think christians did know bible man\n",
+            "Topic # 7\n",
+            "thanks use using does help like display need problem know server screen windows window program\n"
+          ]
+        }
+      ],
+      "source": [
+        "from sklearn.datasets import fetch_20newsgroups\n",
+        "from sklearn.feature_extraction.text import CountVectorizer\n",
+        "from sklearn.decomposition import LatentDirichletAllocation\n",
+        "\n",
+        "# 모터사이클, 야구, 그래픽스, 윈도우즈, 중동, 기독교, 전자공학, 의학 8개 주제를 추출.\n",
+        "cats = ['rec.motorcycles', 'rec.sport.baseball', 'comp.graphics', 'comp.windows.x',\n",
+        "        'talk.politics.mideast', 'soc.religion.christian', 'sci.electronics', 'sci.med']\n",
+        "\n",
+        "# 위에서 cats 변수로 기재된 카테고리만 추출. subset='all'로 오타 수정\n",
+        "news_df = fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes'),\n",
+        "                             categories=cats, random_state=0)\n",
+        "\n",
+        "# LDA는 Count 기반의 벡터화만 적용. max_features=1000 오타 수정\n",
+        "count_vect = CountVectorizer(max_df=0.95, max_features=1000, min_df=2, stop_words='english', ngram_range=(1, 2))\n",
+        "feat_vect = count_vect.fit_transform(news_df.data)\n",
+        "print('CountVectorizer Shape:', feat_vect.shape)\n",
+        "\n",
+        "# LDA 모델 객체 생성 및 학습\n",
+        "lda = LatentDirichletAllocation(n_components=8, random_state=0)\n",
+        "lda.fit(feat_vect)\n",
+        "\n",
+        "print(lda.components_.shape)\n",
+        "\n",
+        "# 토픽별 핵심 단어 출력 함수\n",
+        "def display_topics(model, feature_names, no_top_words):\n",
+        "    for topic_index, topic in enumerate(model.components_):\n",
+        "        print('Topic #', topic_index)\n",
+        "        # components_ array에서 가장 값이 큰 순으로 정렬했을 때, 그 값의 array 인덱스를 반환.\n",
+        "        topic_word_indexes = topic.argsort()[::-1]\n",
+        "        top_indexes = topic_word_indexes[:no_top_words]\n",
+        "\n",
+        "        # top_indexes 대상인 인덱스별로 feature_names에 해당하는 word feature 추출 후 join으로 concat\n",
+        "        feature_concat = ' '.join([feature_names[i] for i in top_indexes])\n",
+        "        print(feature_concat)\n",
+        "\n",
+        "# CountVectorizer 객체 내의 전체 word의 명칭을 get_feature_names_out()를 통해 추출 (최신 버전 반영)\n",
+        "feature_names = count_vect.get_feature_names_out()\n",
+        "\n",
+        "# 토픽별 가장 연관도가 높은 word를 15개만 추출\n",
+        "display_topics(lda, feature_names, 15)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##2. Opinion Review 데이터 세트 로딩 및 문서 군집화 (K-Means)"
+      ],
+      "metadata": {
+        "id": "EloQ8slaNtUu"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 1. 데이터 세트 zip 파일 다운로드\n",
+        "!wget https://archive.ics.uci.edu/static/public/288/opinosis+opinion+frasl+review.zip\n",
+        "\n",
+        "# 2. 압축 해제 (opinosis_data 폴더 내에 저장)\n",
+        "!unzip -q opinosis+opinion+frasl+review.zip -d opinosis_data"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "RF7ry3viObsN",
+        "outputId": "e6321346-4b13-4aa0-879b-8bedb1f43dbd"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "--2026-05-26 12:19:29--  https://archive.ics.uci.edu/static/public/288/opinosis+opinion+frasl+review.zip\n",
+            "Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.252\n",
+            "Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.252|:443... connected.\n",
+            "HTTP request sent, awaiting response... 404 Not Found\n",
+            "2026-05-26 12:19:30 ERROR 404: Not Found.\n",
+            "\n",
+            "unzip:  cannot find or open opinosis+opinion+frasl+review.zip, opinosis+opinion+frasl+review.zip.zip or opinosis+opinion+frasl+review.zip.ZIP.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import glob, os\n",
+        "import warnings\n",
+        "import nltk\n",
+        "import string\n",
+        "from nltk.stem import WordNetLemmatizer\n",
+        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+        "from sklearn.cluster import KMeans\n",
+        "\n",
+        "warnings.filterwarnings('ignore')\n",
+        "pd.set_option('display.max_colwidth', 700)\n",
+        "\n",
+        "# 다운로드에 필요한 nltk 데이터\n",
+        "nltk.download('punkt')\n",
+        "nltk.download('wordnet')\n",
+        "\n",
+        "# 책 부록에 생략된 LemNormalize 함수 구현 (실행을 위해 추가)\n",
+        "remove_punct_dict = dict((ord(punct), None) for punct in string.punctuation)\n",
+        "lemmar = WordNetLemmatizer()\n",
+        "\n",
+        "def LemTokens(tokens):\n",
+        "    return [lemmar.lemmatize(token) for token in tokens]\n",
+        "\n",
+        "def LemNormalize(text):\n",
+        "    return LemTokens(nltk.word_tokenize(text.lower().translate(remove_punct_dict)))\n",
+        "\n",
+        "path = '/content/opinosis_data/OpinosisDataset1.0/topics'\n",
+        "\n",
+        "all_files = glob.glob(os.path.join(path, \"*.data\"))\n",
+        "filename_list = []\n",
+        "opinion_text = []\n",
+        "\n",
+        "# 파일 취합 및 DataFrame 로딩\n",
+        "for file_ in all_files:\n",
+        "    df = pd.read_table(file_, index_col=None, header=0, encoding='latin1')\n",
+        "    # 코랩(Linux) 환경과 윈도우 환경 모두 동작하도록 os.path.basename 사용\n",
+        "    filename_ = os.path.basename(file_)\n",
+        "    filename = filename_.split('.')[0]\n",
+        "\n",
+        "    filename_list.append(filename)\n",
+        "    opinion_text.append(df.to_string())\n",
+        "\n",
+        "document_df = pd.DataFrame({'filename':filename_list, 'opinion_text':opinion_text})\n",
+        "\n",
+        "# TF-IDF 피처 벡터화 적용\n",
+        "tfidf_vect = TfidfVectorizer(tokenizer=LemNormalize, stop_words='english',\n",
+        "                             ngram_range=(1,2), min_df=0.05, max_df=0.85)\n",
+        "\n",
+        "# (주의: 실제 데이터가 있어야 아래 코드 실행 가능)\n",
+        "# feature_vect = tfidf_vect.fit_transform(document_df['opinion_text'])\n",
+        "\n",
+        "# 3개의 집합으로 군집화 수행\n",
+        "# km_cluster = KMeans(n_clusters=3, max_iter=10000, random_state=0)\n",
+        "# km_cluster.fit(feature_vect)\n",
+        "# document_df['cluster_label'] = km_cluster.labels_\n",
+        "# document_df.sort_values(by='cluster_label')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cdX_GCmJOgJc",
+        "outputId": "7364ada8-0fd2-41dd-c552-00a2ecf35c29"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
+            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
+            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##3. 군집별 핵심 단어 추출"
+      ],
+      "metadata": {
+        "id": "8e634y1bPAJx"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 군집별 top n 핵심 단어, 그 단어의 중심 위치 상댓값, 대상 파일명을 반환함.\n",
+        "def get_cluster_details(cluster_model, cluster_data, feature_names, clusters_num, top_n_features=10):\n",
+        "    cluster_details = {}\n",
+        "    # cluster_centers_ array의 값이 큰 순으로 정렬된 인덱스 값을 반환\n",
+        "    centroid_feature_ordered_ind = cluster_model.cluster_centers_.argsort()[:, ::-1]\n",
+        "\n",
+        "    for cluster_num in range(clusters_num):\n",
+        "        cluster_details[cluster_num] = {}\n",
+        "        cluster_details[cluster_num]['cluster'] = cluster_num\n",
+        "\n",
+        "        top_feature_indexes = centroid_feature_ordered_ind[cluster_num, :top_n_features]\n",
+        "        top_features = [feature_names[ind] for ind in top_feature_indexes]\n",
+        "        top_feature_values = cluster_model.cluster_centers_[cluster_num, top_feature_indexes].tolist()\n",
+        "\n",
+        "        # DataFrame에서 클러스터 번호에 해당하는 파일명 추출 (조건식 == 오타 수정)\n",
+        "        filenames = cluster_data[cluster_data['cluster_label'] == cluster_num]['filename']\n",
+        "        filenames = filenames.values.tolist()\n",
+        "\n",
+        "        cluster_details[cluster_num]['top_features'] = top_features\n",
+        "        cluster_details[cluster_num]['top_features_value'] = top_feature_values\n",
+        "        cluster_details[cluster_num]['filenames'] = filenames\n",
+        "\n",
+        "    return cluster_details\n",
+        "\n",
+        "def print_cluster_details(cluster_details):\n",
+        "    for cluster_num, cluster_detail in cluster_details.items():\n",
+        "        print('####### Cluster {0}'.format(cluster_num))\n",
+        "        print('Top features:', cluster_detail['top_features'])\n",
+        "        print('Reviews 파일명:', cluster_detail['filenames'][:7])\n",
+        "        print('==================================================')\n",
+        "\n",
+        "# (실제 실행을 위한 코드 예시 - 주석 처리됨)\n",
+        "# feature_names = tfidf_vect.get_feature_names_out()\n",
+        "# cluster_details = get_cluster_details(cluster_model=km_cluster, cluster_data=document_df,\n",
+        "#                                       feature_names=feature_names, clusters_num=3, top_n_features=10)\n",
+        "# print_cluster_details(cluster_details)"
+      ],
+      "metadata": {
+        "id": "CEXwMPTrOrcZ"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##4. 문서 유사도 측정 (Cosine Similarity)"
+      ],
+      "metadata": {
+        "id": "VcnH9U9hO74m"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+        "from sklearn.metrics.pairwise import cosine_similarity\n",
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt\n",
+        "%matplotlib inline\n",
+        "\n",
+        "# 1. 수식으로 직접 구현하는 코사인 유사도 함수\n",
+        "def cos_similarity(v1, v2):\n",
+        "    dot_product = np.dot(v1, v2)\n",
+        "    l2_norm = (np.sqrt(sum(np.square(v1))) * np.sqrt(sum(np.square(v2))))\n",
+        "    similarity = dot_product / l2_norm\n",
+        "    return similarity\n",
+        "\n",
+        "doc_list = ['if you take the blue pill, the story ends',\n",
+        "            'if you take the red pill, you stay in Wonderland',\n",
+        "            'if you take the red pill, I show you how deep the rabbit hole goes']\n",
+        "\n",
+        "tfidf_vect_simple = TfidfVectorizer()\n",
+        "feature_vect_simple = tfidf_vect_simple.fit_transform(doc_list)\n",
+        "print('feature_vect_simple shape:', feature_vect_simple.shape)\n",
+        "\n",
+        "# 밀집 행렬 변환 후 수동 함수로 계산 테스트\n",
+        "feature_vect_dense = feature_vect_simple.todense()\n",
+        "vect1 = np.array(feature_vect_dense[0]).reshape(-1,)\n",
+        "vect2 = np.array(feature_vect_dense[1]).reshape(-1,)\n",
+        "similarity_simple = cos_similarity(vect1, vect2)\n",
+        "print('문장 1, 문장 2 Cosine 유사도: {0:.3f}'.format(similarity_simple))\n",
+        "\n",
+        "# 2. 사이킷런의 cosine_similarity API 활용\n",
+        "similarity_simple_pair = cosine_similarity(feature_vect_simple[0], feature_vect_simple)\n",
+        "print('사이킷런 API 첫번째 문서 유사도:\\n', similarity_simple_pair)\n",
+        "\n",
+        "similarity_all_pair = cosine_similarity(feature_vect_simple, feature_vect_simple)\n",
+        "print('사이킷런 API 전체 문서 유사도:\\n', similarity_all_pair)\n",
+        "\n",
+        "# (참고) 이전 리뷰 데이터의 특정 군집 간 유사도 비교 시각화 (코드 템플릿)\n",
+        "# sorted_index = similarity_pair.argsort()[:, ::-1]\n",
+        "# sorted_index = sorted_index[:, 1:] # 자기 자신 제외\n",
+        "# sns.barplot(x='similarity', y='filename', data=hotel_1_sim_df)\n",
+        "# plt.title(comparison_docname)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "FRmdn6c_Osy2",
+        "outputId": "7e5a5d7e-4dbb-44a8-f326-ffd959b58171"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "feature_vect_simple shape: (3, 18)\n",
+            "문장 1, 문장 2 Cosine 유사도: 0.402\n",
+            "사이킷런 API 첫번째 문서 유사도:\n",
+            " [[1.         0.40207758 0.40425045]]\n",
+            "사이킷런 API 전체 문서 유사도:\n",
+            " [[1.         0.40207758 0.40425045]\n",
+            " [0.40207758 1.         0.45647296]\n",
+            " [0.40425045 0.45647296 1.        ]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##5. 한글 형태소 분석 및 네이버 영화 평점 감성 분석"
+      ],
+      "metadata": {
+        "id": "NSy7IbzjO4iR"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 학습 데이터(ratings_train.txt) 및 테스트 데이터(ratings_test.txt) 다운로드\n",
+        "!wget https://raw.githubusercontent.com/e9t/nsmc/master/ratings_train.txt\n",
+        "!wget https://raw.githubusercontent.com/e9t/nsmc/master/ratings_test.txt"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "yv-MlWrqPEZx",
+        "outputId": "665dbfd6-a973-4882-e6bd-8cb608b48856"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "--2026-05-26 12:22:14--  https://raw.githubusercontent.com/e9t/nsmc/master/ratings_train.txt\n",
+            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.110.133, 185.199.111.133, ...\n",
+            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 14628807 (14M) [text/plain]\n",
+            "Saving to: ‘ratings_train.txt’\n",
+            "\n",
+            "ratings_train.txt   100%[===================>]  13.95M  --.-KB/s    in 0.1s    \n",
+            "\n",
+            "2026-05-26 12:22:14 (108 MB/s) - ‘ratings_train.txt’ saved [14628807/14628807]\n",
+            "\n",
+            "--2026-05-26 12:22:14--  https://raw.githubusercontent.com/e9t/nsmc/master/ratings_test.txt\n",
+            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.110.133, 185.199.111.133, ...\n",
+            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 4893335 (4.7M) [application/octet-stream]\n",
+            "Saving to: ‘ratings_test.txt’\n",
+            "\n",
+            "ratings_test.txt    100%[===================>]   4.67M  --.-KB/s    in 0.07s   \n",
+            "\n",
+            "2026-05-26 12:22:15 (67.2 MB/s) - ‘ratings_test.txt’ saved [4893335/4893335]\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 패키지 설치\n",
+        "!pip install konlpy\n",
+        "\n",
+        "import pandas as pd\n",
+        "import re\n",
+        "from konlpy.tag import Okt\n",
+        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.model_selection import GridSearchCV\n",
+        "from sklearn.metrics import accuracy_score\n",
+        "\n",
+        "# 데이터 로딩\n",
+        "try:\n",
+        "    train_df = pd.read_csv('ratings_train.txt', sep='\\t', encoding='utf-8')\n",
+        "    test_df = pd.read_csv('ratings_test.txt', sep='\\t', encoding='utf-8')\n",
+        "except FileNotFoundError:\n",
+        "    print(\"데이터 파일이 없습니다.\")\n",
+        "\n",
+        "# 전처리\n",
+        "if 'train_df' in locals():\n",
+        "    train_df = train_df.fillna(' ')\n",
+        "    train_df['document'] = train_df['document'].apply(lambda x: re.sub(r\"\\d+\", \" \", x))\n",
+        "    train_df.drop('id', axis=1, inplace=True)\n",
+        "\n",
+        "    test_df = test_df.fillna(' ')\n",
+        "    test_df['document'] = test_df['document'].apply(lambda x: re.sub(r\"\\d+\", \" \", x))\n",
+        "    test_df.drop('id', axis=1, inplace=True)\n",
+        "\n",
+        "# 토크나이저 설정\n",
+        "okt = Okt()\n",
+        "def tw_tokenizer(text):\n",
+        "    return okt.morphs(text)\n",
+        "\n",
+        "# TF-IDF 벡터화 및 모델 학습 수행\n",
+        "tfidf_vect = TfidfVectorizer(tokenizer=tw_tokenizer, ngram_range=(1, 2), min_df=3, max_df=0.9)\n",
+        "tfidf_vect.fit(train_df['document'])\n",
+        "tfidf_matrix_train = tfidf_vect.transform(train_df['document'])\n",
+        "\n",
+        "lg_clf = LogisticRegression(random_state=0, solver='liblinear')\n",
+        "params = {'C': [1, 3.5, 4.5, 5.5, 10]}\n",
+        "\n",
+        "grid_cv = GridSearchCV(lg_clf, param_grid=params, cv=3, scoring='accuracy', verbose=1)\n",
+        "grid_cv.fit(tfidf_matrix_train, train_df['label'])\n",
+        "print(grid_cv.best_params_, round(grid_cv.best_score_, 4))\n",
+        "\n",
+        "# 테스트 데이터 예측\n",
+        "tfidf_matrix_test = tfidf_vect.transform(test_df['document'])\n",
+        "best_estimator = grid_cv.best_estimator_\n",
+        "preds = best_estimator.predict(tfidf_matrix_test)\n",
+        "print('최종 정확도:', accuracy_score(test_df['label'], preds))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_UkpfiUdPFv6",
+        "outputId": "ca107864-100d-4421-9bd9-1b55543ad68a"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: konlpy in /usr/local/lib/python3.12/dist-packages (0.6.0)\n",
+            "Requirement already satisfied: JPype1>=0.7.0 in /usr/local/lib/python3.12/dist-packages (from konlpy) (1.7.1)\n",
+            "Requirement already satisfied: lxml>=4.1.0 in /usr/local/lib/python3.12/dist-packages (from konlpy) (6.1.0)\n",
+            "Requirement already satisfied: numpy>=1.6 in /usr/local/lib/python3.12/dist-packages (from konlpy) (2.0.2)\n",
+            "Requirement already satisfied: packaging in /usr/local/lib/python3.12/dist-packages (from JPype1>=0.7.0->konlpy) (26.1)\n",
+            "Fitting 3 folds for each of 5 candidates, totalling 15 fits\n",
+            "{'C': 3.5} 0.8593\n",
+            "최종 정확도: 0.86172\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
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