diff --git a/购物评论 情感分类/彭宇昊 短信识别/NLP垃圾短信识别.py b/购物评论 情感分类/彭宇昊 短信识别/NLP垃圾短信识别.py
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+++ b/购物评论 情感分类/彭宇昊 短信识别/NLP垃圾短信识别.py	
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+import pandas as pd
+import re
+import jieba
+import pickle
+from wordcloud import WordCloud#安装wordcloud要输入后面的那个，而不是前面的，就像安装opencv，要装cv2
+from sklearn.naive_bayes import GaussianNB
+from sklearn.model_selection import train_test_split
+from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
+from sklearn.metrics import confusion_matrix
+import seaborn as sns
+import matplotlib.pyplot as plt
+plt.rcParams['font.sans-serif'] = 'SimHei'
+plt.rcParams['axes.unicode_minus'] = False
+data = pd.read_csv('message80W1.csv', header=None, index_col=0)
+data.columns = ['label', 'message']
+n = 5000
+a = data[data['label'] == 0].sample(n)
+b = data[data['label'] == 1].sample(n)
+newdata = pd.concat([a, b], axis=0)
+dupdata = newdata['message'].drop_duplicates()
+qumindata = dupdata.apply(lambda x: re.sub('x', '', x))
+# 读取自定义的分词词典
+jieba.load_userdict('newdic1.txt')
+data_cut = qumindata.apply(lambda x: jieba.lcut(x))
+# 读取停用词表去停用词
+stopWords = pd.read_csv('stopword.txt', encoding='GB18030', sep='hahaha', header=None)
+stopWords = ['≮', '≯', '≠', '≮', ' ', '会', '月', '日', '–'] + list(stopWords.iloc[:, 0])
+dataafterstop = data_cut.apply(lambda x: [i for i in x if i not in stopWords])
+labels = newdata.loc[dataafterstop.index, 'label']
+# join函数将分词后的数据转换为字符串
+adata = dataafterstop.apply(lambda x: ' '.join(x))
+# 划分数据集
+data_tr, data_te, labels_tr, labels_te = train_test_split(adata, labels, test_size=0.15)
+# 转词向量
+countVectorizer = CountVectorizer()
+# 训练集
+data_tr = countVectorizer.fit_transform(data_tr)
+X_tr = TfidfTransformer().fit_transform(data_tr.toarray()).toarray()
+# 测试集
+data_te = CountVectorizer(vocabulary=countVectorizer.vocabulary_).fit_transform(data_te)
+X_te = TfidfTransformer().fit_transform(data_te.toarray()).toarray()
+model = GaussianNB()
+model.fit(X_tr, labels_tr)
+pred = model.predict(X_te)
+score = model.score(X_te, labels_te)
+# 使用pickle模块保存模型
+with open('model.pkl', 'wb') as f:
+    pickle.dump(model, f)
+# 保存词向量模型
+with open('countVectorizer.pkl', 'wb') as f:
+    pickle.dump(countVectorizer, f)
+# 绘制混淆矩阵
+sns.heatmap(confusion_matrix(labels_te,pred),annot=True)
+plt.xlabel('Predicted')
+plt.ylabel('True')
+plt.title("混淆矩阵")
+plt.show()
+## 绘制词云
+word_fre = {}
+for i in dataafterstop[labels == 0]:
+    for j in i:
+        if j not in word_fre.keys():
+            word_fre[j] = 1
+        else:
+            word_fre[j] += 1
+wc = WordCloud( background_color='white', font_path=r'C:/Windows/Fonts/STKAITI.TTF')#换一个长方形的边框，同时变成华文楷体
+wc.fit_words(word_fre)
+plt.imshow(wc)
+plt.show()
diff --git a/购物评论 情感分类/彭宇昊 短信识别/混淆矩阵.png b/购物评论 情感分类/彭宇昊 短信识别/混淆矩阵.png
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