import tweepy

Consumer_Key="NgFBmwrYkSTMw3TsNxEj5GO4w"

Consumer_Secret="2rrFsWt8UmQoZgxnIFimIsAP44XCVs2cKIeI3Zt7sdZOmXkjr5"

Access_Token="608860877-SvmmykP5RY9YfDHS9i2kWgH7hS7NM17gkj50GQwW"

Access_Secret="O5ycdf4QspwSSxVQCPEvSy8gJGi2fYp9psSs3VsT0rxJV"

auth=tweepy.OAuthHandler(Consumer_Key,Consumer_Secret)

auth.set_access_token(Access_Token,Access_Secret)

api=tweepy.API(auth)

"""

#时间线

public_tweets=api.home_timeline(1000)

for tweet in public_tweets:

print(tweet.text)

#指定id

id='cctv5five'

content=[]

results=api.user_timeline(id,count=1000,lang='en')

for tweet in results:

content.append(tweet.text)

"""

#关键字查找

query="investment"

language="en"

words=api.search(q=query,lang=language,count=1000)

content=[]

for tweet in words:

content.append(tweet.text)

import numpy as np

import pandas as pd

import nltk

from nltk import word_tokenize

import re

from nltk.corpus import stopwords

stop=stopwords.words('english')

from nltk.stem.porter import*

stemmer=PorterStemmer()

words1=[word_tokenize(i) for i in content]

words2=[[i.lower() for i in doc] for doc in words1]

words3=[[i for i in doc if re.search('^[a-z]+$',i)] for doc in words2]

words4=[[i for i in doc if i not in stop] for doc in words3]

words5=[[stemmer.stem(i) for i in doc] for doc in words4]

text=[]

for i in range(len(words5)):

for j in words5[i]:

text.append(j)

from sklearn.feature_extraction.text import CountVectorizer

vec=CountVectorizer()

vec.fit(text)

vec.vocabulary_

vec.vocabulary_.get(u'algorithm')

vec.get_feature_names()

vec.build_analyzer()

vec.build_tokenizer()

vec.tokenizer()