机器学习-情感分析小案例

叁歲伎倆 2021-11-17 10:48 318阅读 0赞

##### 对发帖情感进行分析。 #####

**字段说明：** Announce\_ID字段代表用户ID，User\_Name字段代表用户名，topic字段代表发帖主题，body字段代表发帖内容，post\_type字段代表发帖话题是否与工作相关，sentiment字段表明发帖情感色彩，IP字段代表用户IP地址。

关于[classify.xls][]文件 提取码：fkwq

[stopwords.txt][] 文件 提取码：05z1

相关代码

import pandas as pd
    import jieba
    from sklearn.feature_extraction.text import CountVectorizer
    from sklearn.decomposition import TruncatedSVD
    from sklearn import model_selection, metrics
    from sklearn.naive_bayes import BernoulliNB
    # 读入classify文件，Sheetname设为classify
    df = pd.read_excel('classify.xls', sheet_name='classify')
    # print(df)
    # 对post_type特征做频数统计
    # print(df['post_type'].value_counts())
    # 删去User_Name、post_type、IP特征
    df.drop(columns=['User_Name', 'post_type', 'IP'], inplace=True)
    # print(df)
    # 对Body特征进行中文分词处理
    df['Body'] = df['Body'].map(lambda x:jieba.lcut(x))
    # print(df['Body'].head())
    # 读取停用词表，剔除停用词
    s = ''
    with open('stopwords.txt', 'r', encoding='utf8') as r:
        for i in r.readlines():
            s += i.strip()
    
    df['Body'] = df['Body'].map(lambda x:[i for i in x if(i not in s) and (len(i) > 1)])
    # print(df['Body'].head())
    # 将剔除停用词后的Body特征转换为矩阵向量
    list1 = df['Body'].map(lambda line:' '.join(line))
    cv = CountVectorizer()
    w = cv.fit_transform(list1)
    # 降维处理得到特征X
    svd = TruncatedSVD(50)
    X = svd.fit_transform(w)
    # 切片sentiment特征作为目标标签Y
    Y = df['sentiment']
    # 按照8：2划分数据集
    train_x, test_x, train_y, test_y = model_selection.train_test_split(X, Y, test_size=0.2, random_state=0)
    # 建立朴素贝叶斯分类模型
    model = BernoulliNB()
    model.fit(train_x, train_y)
    # 进行模型训练和预测
    h = model.predict(test_x)
    # 打印混淆矩阵和分类报告
    print('classification_report:\n', metrics.classification_report(test_y, h))
    print('confusion_matrix:\n', metrics.confusion_matrix(test_y, h))

[classify.xls]: https://pan.baidu.com/s/1ok0wmGrLxnuSHgNLj9935A
[stopwords.txt]: https://pan.baidu.com/s/1ih08qfV5pJwkSQr0-GcRhg