Python 分析Twitter用户喜爱的推文
宰父涵忍
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)RESULT:
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 | ن |
| 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
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