CODE:
#!/usr/bin/python
# -*- coding: utf-8 -*-
'''
Created on 2014-8-5
@author: guaguastd
@name: analyze_favorite_tweet.py
'''
if __name__ == '__main__':
# import json
#import json
# import search
from search import search_for_tweet
# import get_friends_followers_ids
from user import crawl_followers
# import login, see http://blog.csdn.net/guaguastd/article/details/31706155
from login import twitter_login
# import tweet
from tweet import analyze_favorites_tweet
# get the twitter access api
twitter_api = twitter_login()
# import twitter_text
import twitter_text
while 1:
query = raw_input('\nInput the query (eg. #MentionSomeoneImportantForYou, exit to quit): ')
if query == 'exit':
print 'Successfully exit!'
break
statuses = search_for_tweet(twitter_api, query)
ex = twitter_text.Extractor(statuses)
screen_names = ex.extract_mentioned_screen_names_with_indices()
screen_names = [screen_name['screen_name']
for screen_name in screen_names]
for screen_name in screen_names:
#print json.dumps(screen_names, indent=1)
analyze_favorites_tweet(twitter_api, screen_name)Input the query (eg. #MentionSomeoneImportantForYou, exit to quit): Core Python Length of statuses 59 Number of favorites: 200 Common entities in favorites... +--------+------------------------+ | Entity | Count | +--------+------------------------+ | 72 | the | | 72 | to | | 57 | a | | 56 | of | | 53 | in | | 44 | on | | 37 | is | | 36 | for | | 34 | and | | 29 | I | | 28 | you | | 24 | my | | 21 | - | | 21 | at | | 19 | with | | 17 | be | | 17 | by | | 15 | talk | | 15 | are | | 15 | from | | 14 | The | | 14 | this | | 13 | can | | 13 | that | | 13 | snim2 | | 12 | @snim2 | | 12 | an | | 11 | Python | | 11 | your | | 11 | about | | 10 | it | | 10 | was | | 10 | all | | 10 | ep14 | | 9 | europython | | 9 | now | | 9 | or | | 8 | via | | 7 | A | | 7 | Here | | 7 | if | | 7 | not | | 7 | our | | 7 | have | | 7 | who | | 7 | #ep14 | | 7 | as | | 6 | new | | 6 | me | | 6 | just | | 6 | #europython | | 6 | slides | | 6 | & | | 5 | concurrency | | 5 | My | | 5 | IPython | | 5 | so | | 5 | more | | 5 | paper | | 5 | also | | 5 | most | | 5 | see | | 5 | available | | 5 | video | | 5 | write | | 5 | out | | 5 | piece | | 5 | software | | 4 | has | | 4 | when | | 4 | :) | | 4 | Research | | 4 | here: | | 4 | take | | 4 | If | | 4 | being | | 4 | code | | 4 | what | | 4 | help | | 4 | really | | 4 | For | | 4 | some | | 4 | up | | 4 | python | | 4 | This | | 4 | based | | 4 | will | | 4 | You | | 4 | he | | 3 | Haskell | | 3 | @europython | | 3 | much | | 3 | photo | | 3 | #python | | 3 | easy | | 3 | post | | 3 | own | | 3 | #LGBT | | 3 | papers | | 3 | time | | 3 | Our | | 3 | Why | | 3 | answer | | 3 | first | | 3 | one | | 3 | open | | 3 | than | | 3 | ep2014 | | 3 | get | | 3 | LGBT | | 3 | Gaza | | 3 | read | | 3 | Slides | | 3 | presentation | | 3 | large | | 3 | learned | | 3 | learn | | 3 | don't | | 3 | good | | 3 | did | | 3 | Thanks | | 3 | like | | 3 | tweets/second | | 3 | his | | 3 | wrote | | 3 | please | | 3 | Software | | 3 | analysis | | 3 | Here's | | 3 | .. | | 3 | An | | 3 | great | | 3 | use | | 3 | | | | 3 | EuroPython | | 3 | you're | | 3 | their | | 3 | but | | 3 | why | | 3 | should | | 3 | means | | 3 | #ep2014 | | 3 | keynote | | 3 | day | | 3 | know | | 3 | because | | 3 | Great | | 2 | under | | 2 | Amazon | | 2 | Church | | 2 | Group | | 2 | aware | | 2 | must | | 2 | want | | 2 | how | | 2 | interview | | 2 | after | | 2 | things | | 2 | feedback | | 2 | over | | 2 | them | | 2 | Check | | 2 | Shakira | | 2 | got | | 2 | messages | | 2 | days | | 2 | Please | | 2 | Notebook | | 2 | @parallellaboard | | 2 | “Can | | 2 | mine | | 2 | Twisted | | 2 | do | | 2 | #concurrency | | 2 | officially | | 2 | w/ | | 2 | John | | 2 | said | | 2 | never | | 2 | I've | | 2 | been | | 2 | twistedmatrix | | 2 | make | | 2 | jobs. | | 2 | #EuroPython | | 2 | Use | | 2 | way | | 2 | role | | 2 | test | | 2 | update | | 2 | parallellaboard | | 2 | daily | | 2 | Just | | 2 | MT | | 2 | MP | | 2 | It's | | 2 | following | | 2 | may | | 2 | Model | | 2 | switch | | 2 | RT | | 2 | tweets | | 2 | WeAreN | | 2 | name | | 2 | attended | | 2 | programming | | 2 | think | | 2 | message | | 2 | short | | 2 | Do | | 2 | online | | 2 | science, | | 2 | #WeAreN | | 2 | going | | 2 | Growth | | 2 | where | | 2 | #synod | | 2 | 3 | | 2 | jobs | | 2 | many | | 2 | Jeremy | | 2 | those | | 2 | these | | 2 | engineering | | 2 | GNU | | 2 | different | | 2 | surveillance | | 2 | week | | 2 | blog | | 2 | LindaWoodhead | | 2 | start | | 2 | ? | | 2 | How | | 2 | watched | | 2 | trash | | 2 | #Python | | 2 | coverage | | 2 | @LindaWoodhead | | 2 | remote | | 2 | consider | | 2 | program | | 2 | very | | 2 | St | | 2 | Your | | 2 | github | | 2 | that's | | 2 | its | | 2 | it. | | 2 | it: | | 2 | c_of_e | | 2 | research | | 2 | together | | 2 | without | | 2 | nothing | | 2 | pre-print | | 2 | during | | 2 | Part | | 2 | last | | 2 | Steve | | 2 | point | | 2 | church | | 2 | Women | | 2 | error | | 2 | arXiv | | 2 | http://t.co/0yBSWrVaUW | | 2 | person | | 2 | Names | | 2 | docker | | 2 | Reproducible | | 2 | source | | 2 | popular | | 2 | back | | 2 | @twistedmatrix | | 2 | am | | 2 | < | | 2 | @PyConUK | | 2 | AV | | 2 | Implement | | 2 | asyncio | | 2 | Git | | 2 | try | | 2 | making | | 2 | involved | | 2 | Algorithm?”: | | 2 | tools | | 2 | … | | 2 | Video | | 2 | links | | 2 | profile | | 2 | lines | | 2 | One | | 2 | 2015 | | 2 | Can | | 2 | lecture | | 2 | data | | 2 | need | | 2 | which | | 2 | Some | | 2 | Bishop | | 2 | fact | | 2 | local | | 2 | computer | | 2 | Good | | 2 | synod | | 2 | passing | | 2 | it's | | 2 | PyConUK | | 2 | #asyncio | | 2 | intro | | 2 | Oxford | | 2 | single | | 2 | latest | | 2 | CofE | | 2 | async | | 2 | Telegraph | | 2 | growth | | 2 | Science | | 2 | problem | | 2 | this: | +--------+------------------------+ Some statistics about the content of the favorities... Lexical diversity (words): 0.605255023184 Lexical diversity (screen names): 1.0 Lexical diversity (hashtags): 0.831932773109 Averge words per tweet: 16.175 Number of favorites: 2 Common entities in favorites... +--------+-------------+ | Entity | Count | +--------+-------------+ | 2 | @AndersInno | | 2 | AndersInno | +--------+-------------+ Some statistics about the content of the favorities... Lexical diversity (words): 0.9375 Lexical diversity (screen names): 1.0 Lexical diversity (hashtags): 0.75 Averge words per tweet: 8.0 Number of favorites: 6 Common entities in favorites... +--------+-------+ | Entity | Count | +--------+-------+ | 4 | the | | 3 | to | | 2 | be | | 2 | of | | 2 | this | | 2 | is | | 2 | in | | 2 | I | | 2 | a | +--------+-------+ Some statistics about the content of the favorities... Lexical diversity (words): 0.872340425532 Lexical diversity (screen names): 1.0 Lexical diversity (hashtags): 1.0 Averge words per tweet: 15.6666666667 Number of favorites: 0 Common entities in favorites... +--------+-------+ | Entity | Count | +--------+-------+ +--------+-------+ Some statistics about the content of the favorities... No statuses to analyze
Python 分析Twitter用户喜爱的推文,布布扣,bubuko.com
原文地址：http://blog.csdn.net/guaguastd/article/details/38378857
