NLP中kaggle比赛实例《每日新闻对股票市场的预测》进阶版

这篇比基础版加了什么呢？

基础版是直接将文本放入TF-IDF,虽然简单方便，但还是不够严谨，可以对数据进行进一步处理，如先小写化，将文本分成晓得tokens，接着删减如the，a，that等停止词，用正则化删除数字。用这些手段将数据进行处理。

from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer
import  pandas as pd
import numpy as pd
from sklearn.svm import SVC
from sklearn.metrics import roc_auc_score
from datetime import date

#导入数据
data = pd.read_csv('')
#将headlines合并起来，考虑所有的news
data['combined_news'] = data.filter(regex=('Top.*')).apply(lambda x:''.join(str(x.values)),axis = 1)
#分割测试/训练集
train = data[data['Date']<'2015-01-01']
test = data[data['Date']>'2014-12-31']
#文本预处理
X_train = train['combined_news'].str.lower().str.replace('"','').str.replace("'",'').str.split()
X_test = test['combined_news'].str.lower().str.replace('"','').str.replace("'",'').str.split()
#删减停止词
from nltk.corpus import stopwords
stop = stopwords.words('english')
#删除数字
import re
def hasNumbers(inputStrings):
    return bool(re.search(r'\d',inputStrings))
#lemma
from nltk.stem import WordNetLemmatizer
wordnet_lemmatizer = WordNetLemmatizer()
def check(word):
    #如果需要这个单词，则True，如果需要去除，则False
    if word in stop:
        return False
    elif hasNumbers(word):
        return False
    else:
        return True
#然后把整个流程放进我们的DF中进行处理
X_train = X_train.apply(lambda x:[wordnet_lemmatizer.lemmatize(item) for item in x if check(item)])
X_train = X_train.apply(lambda x:[wordnet_lemmatizer.lemmatize(item) for item in x if check(item)])
#因为外部库，比如sklearn ，只支持string输入，所以我们把调整后的list再变回string
X_train = X_train.apply(lambda x:''.join(x))
X_test = X_test.apply(lambda x:''.join(x))
#重新fit一遍我们的clf
feature_extraction = TfidfVectorizer(lowercase=False)
X_train =feature_extraction.fit_transform(X_train.values)
X_test =feature_extraction.fit_transform(X_test.values)
#训练模型
clf = SVC(probability=True,kernel='rbf')
clf.fit(X_train,y_train)
predictions = clf.predict_proba(X_test)
print('ROC_AUC yieds'+str(roc_auc_score(y_test,predictions[:,1])))

NLP中kaggle比赛实例《每日新闻对股票市场的预测》进阶版

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