[PYTHON] Information is accumulated by Twitter search, morphological analysis is performed, sentences are generated by Markov chains, and tweets are made.

Save search results on Twitter in text format. Save various search results to text, read the text and perform morphological analysis. From there, generate sentences with Markov chains and tweet POST.

twmarkov.py

#!/user/bin/env python
# -*- coding: utf-8 -*-
from requests_oauthlib import OAuth1Session
import json
import sys
import MeCab
import random
import re

while True:
	search_words = raw_input(u"words: ")
	
	C_KEY = "*************************************"
	C_SECRET = "*************************************"
	A_KEY = "*************************************"
	A_SECRET = "*************************************"

	def Search_words():
		url = "https://api.twitter.com/1.1/search/tweets.json?"
		params = {
				"q": unicode(search_words, "utf-8"),
				"lang": "ja",
				"result_type": "recent",
				"count": "100"
				}
		tw = OAuth1Session(C_KEY,C_SECRET,A_KEY,A_SECRET)
		req = tw.get(url, params = params)
		tweets = json.loads(req.text)
		for tweet in tweets["statuses"]:
			f = open("tweet.txt" , "aw")
			lists = (tweet["text"].encode("utf-8"))
			if "http" in lists:
				lists = lists.split("http", 1)[0]
				lists = lists.split("@")[0]
				lists = lists.split("RT")[0]

				f.write(lists)
				f.flush()
				f.close()

		
	def Mecab_file():	
		f = open("tweet.txt","rb")
		data = f.read()
		f.close()

		mt = MeCab.Tagger("-Owakati")

		wordlist = mt.parse(data)
		wordlist = wordlist.rstrip(" \n").split(" ")
 
		markov = {}
		w = ""
	
		for x in wordlist:
			if w:
				if markov.has_key(w):#Parts that do not work in Python 3
					new_list = markov[w]
				else:
					new_list =[]
			
				new_list.append(x)
				markov[w] = new_list
			w = x
		
		choice_words = wordlist[0]
		sentence = ""
		count = 0
	
		while count < 90:
			sentence += choice_words
			choice_words = random.choice(markov[choice_words])
			count += 1

			sentence = sentence.split(" ", 1)[0]
			p = re.compile("[!-/:-@[-`{-~]")
			sus = p.sub("", sentence)
	
		words = re.sub(re.compile("[!-~]"),"",sus)
		twits = words + " 【tweet from twmarkov】"
		
		url = "https://api.twitter.com/1.1/statuses/update.json"
		params = {"status": twits,"lang": "ja"}
		tw = OAuth1Session(C_KEY,C_SECRET,A_KEY,A_SECRET)
		req = tw.post(url, params = params)
		if req.status_code == 200:
			print "Success! Your Tweet"
		else:
			print req.status_code
	
	
	if search_words:
		Search_words()
		Mecab_file()
	else:
		break

The tweets of the search results are stored more and more in tweet.txt saved as an external file. I'm dissatisfied with the fact that the tweet results aren't interesting if the amount of text is too large or too small.

If you hit it with cron, you can simply make it a bot. In that case, it may be better to pick up hometimeline or usertimeline instead of the search API.

When you run it as a WEB application, it looks like this. (I have removed the program from the server so it is not there now)

Notes on Python 3

if markov.has_key(w):

Is the process

if w in markov:

Replace with.