机器学习实战2--时光网评论分析

# coding: utf-8

# In[74]:

import sys  
reload(sys)  
sys.setdefaultencoding('utf8')
import graphlab
import datetime


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graphlab.set_runtime_config('GRAPHLAB_DEFAULT_NUM_PYLAMBDA_WORKERS', 4)


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products = graphlab.SFrame.read_csv('data/commit.csv')


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products.head()


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#文本分析工具,对评论进行统计分析
products['word_count'] = graphlab.text_analytics.count_words(products['commits'])


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products.head()


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graphlab.canvas.set_target('browser')


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products['movie_name_zh'].show()


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#获取战狼2的点评
giraffe_reviews = products[products['movie_name_zh'] == '战狼2']


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len(giraffe_reviews) #点评数量


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giraffe_reviews['userscore'].show(view='Categorical') #查看分类性质的评分分布
#发现战狼2打7分的人最多


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#忽略5分的评论,忽略没分的评论,分数转为浮点格式
products = products[products['userscore'] != 'None']
products['userscore']
products['userscore'] = products['userscore'].astype(float)
products = products[products['userscore'] != 5]


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#筛选正面评价
products['sentiment'] = products['userscore'] >=6.0


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products.head()


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#区分训练集和测试集,并限定seed
train_data,test_data = products.random_split(.8, seed=0)


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#创建逻辑回归分类器
sentiment_model = graphlab.logistic_classifier.create(train_data,
                                                     target='sentiment',
                                                     features=['word_count'],
                                                     validation_set=test_data)


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#评估情感分析模型.ROC(受试者工作特征曲线)
sentiment_model.evaluate(test_data, metric='roc_curve')


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#可视化评估结果
sentiment_model.show(view='Evaluation')


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#添加新的一列,predicted_sentiment(情感预测),结果概率预测
giraffe_reviews['predicted_sentiment'] = sentiment_model.predict(giraffe_reviews, output_type='probability')


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giraffe_reviews.head()


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#排序,ascending决定是否升序
giraffe_reviews = giraffe_reviews.sort('predicted_sentiment', ascending=False)


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giraffe_reviews.head()


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giraffe_reviews[0]


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giraffe_reviews[2]


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giraffe_reviews[-1]


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giraffe_reviews[-2]


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giraffe_reviews = giraffe_reviews[giraffe_reviews['userscore'] == 'None']


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giraffe_reviews.tail()


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代码地址(附作业答案): https://github.com/RedheatWei/aiproject/tree/master/Machine%20Learning%20Specialization/week3

爬虫地址: https://github.com/RedheatWei/mtime_commit