diff --git a/购物评论 情感分类/短信.ipynb b/购物评论 情感分类/短信.ipynb
deleted file mode 100644
index df39bfd..0000000
--- a/购物评论 情感分类/短信.ipynb	
+++ /dev/null
@@ -1,176 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9d091a01",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import re\n",
-    "import jieba\n",
-    "import pickle\n",
-    "from wordcloud import WordCloud\n",
-    "from sklearn.naive_bayes import GaussianNB\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\n",
-    "from sklearn.metrics import confusion_matrix\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "plt.rcParams['font.sans-serif'] = 'SimHei'\n",
-    "plt.rcParams['axes.unicode_minus'] = False\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "eff92c98",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def data_process(file='message80W1.csv'):\n",
-    "#读取数据并进行清洗\n",
-    "    data = pd.read_csv(file, header=None, index_col=0)\n",
-    "    data.columns = ['label', 'message']\n",
-    "    n = 5000\n",
-    "\n",
-    "    a = data[data['label'] == 0].sample(n)\n",
-    "    b = data[data['label'] == 1].sample(n)\n",
-    "    data_new = pd.concat([a, b], axis=0)\n",
-    "\n",
-    "    data_dup = data_new['message'].drop_duplicates()\n",
-    "    data_qumin = data_dup.apply(lambda x: re.sub('x', '', x))\n",
-    "\n",
-    "    jieba.load_userdict('newdic1.txt')\n",
-    "    data_cut = data_qumin.apply(lambda x: jieba.lcut(x))\n",
-    "\n",
-    "    stopWords = pd.read_csv('stopword.txt', encoding='GB18030', sep='hahaha', header=None)\n",
-    "    stopWords = ['≮', '≯', '≠', '≮', ' ', '会', '月', '日', '–'] + list(stopWords.iloc[:, 0])\n",
-    "    data_after_stop = data_cut.apply(lambda x: [i for i in x if i not in stopWords])\n",
-    "    labels = data_new.loc[data_after_stop.index, 'label']\n",
-    "    adata = data_after_stop.apply(lambda x: ' '.join(x))\n",
-    "    return adata, data_after_stop, labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c3ae80f9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\n",
-    "    sns.heatmap(cm, annot=True)\n",
-    "    plt.ylabel('True label')\n",
-    "    plt.xlabel('Predicted label')\n",
-    "    plt.title(title)\n",
-    "    plt.xticks(np.arange(len(classes)), classes)\n",
-    "    plt.yticks(np.arange(len(classes)), classes)\n",
-    "    plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "56dbb0d1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "adata, data_after_stop, lables = data_process()\n",
-    "data_tr, data_te, labels_tr, labels_te = train_test_split(adata, lables, test_size=0.2)\n",
-    "countVectorizer = CountVectorizer()\n",
-    "data_tr = countVectorizer.fit_transform(data_tr)\n",
-    "X_tr = TfidfTransformer().fit_transform(data_tr.toarray()).toarray()\n",
-    "data_te = CountVectorizer(vocabulary=countVectorizer.vocabulary_).fit_transform(data_te)\n",
-    "X_te = TfidfTransformer().fit_transform(data_te.toarray()).toarray()\n",
-    "model = GaussianNB()\n",
-    "model.fit(X_tr, labels_tr)\n",
-    "pred = model.predict(X_te)\n",
-    "score = model.score(X_te, labels_te)\n",
-    "with open('model.pkl', 'wb') as f:\n",
-    "    pickle.dump(model, f)\n",
-    "with open('countVectorizer.pkl', 'wb') as f:\n",
-    "    pickle.dump(countVectorizer, f)\n",
-    "plot_confusion_matrix(confusion_matrix(labels_te, pred), [1, 0], title=\"模型分类准确率{:.2f}%\".format(score * 100))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cc83543e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "word_fre = {}\n",
-    "for i in data_after_stop[lables == 0]:\n",
-    "    for j in i:\n",
-    "        if j not in word_fre.keys():\n",
-    "            word_fre[j] = 1\n",
-    "        else:\n",
-    "            word_fre[j] += 1\n",
-    "\n",
-    "wc = WordCloud( background_color='white', font_path=r'C:/Windows/Fonts/SimHei.ttf')\n",
-    "wc.fit_words(word_fre)\n",
-    "plt.imshow(wc)\n",
-    "plt.show()\n",
-    "word_fre = {}\n",
-    "for i in data_after_stop[lables == 0]:\n",
-    "    for j in i:\n",
-    "        if j not in word_fre.keys():\n",
-    "            word_fre[j] = 1\n",
-    "        else:\n",
-    "            word_fre[j] += 1\n",
-    "\n",
-    "wc = WordCloud( background_color='white', font_path=r'C:/Windows/Fonts/SimHei.ttf')\n",
-    "wc.fit_words(word_fre)\n",
-    "plt.imshow(wc)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bde6f19c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5f089b32",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}