Try morphological analysis and Markov chains with Django (Ari with a lot of room for improvement)

Introduction

Acquired Twitter data long ago and Markov chain. I did that, but I tried to incorporate it into Django. First of all, the result is a big problem ant. I will leave the progress of the work at the moment.

Click here for past articles Accumulate information by Twitter search, analyze morphological elements, generate sentences with Markov chains, and tweet.

Since .has_key cannot be used in Python3,

if markov.has_key(w):

Is the process

if w in markov:

It is an image to rewrite.

Preparing to use MeCab with Python 3

$ brew install mecab
$ brew install mecab-ipadic
$ pip install mecab-python3

I installed it in this way.

code

As usual, I will go get the RSS of the DMM18 prohibited video.

Simple RSS reader made with Django We are doing almost the same thing as above, so please refer to it.

Get and save the title and description.

views.py

import feedparser
import MeCab
import random
import re
import sys, codecs
sys.stdout = codecs.getwriter('utf-8')(sys.stdout)

from django.http import HttpResponse
from django.shortcuts import (render, redirect,)

def index(request):
    url = 'http://www.dmm.co.jp/digital/videoa/-/list/rss/=/sort=date/'
    feeder = feedparser.parse(url)


    for entry in feeder['entries']:
        lists = entry['description'] + entry['title']
        f = open('text.txt', 'w')
        f.write(lists)
        f.flush()
        f.close()

    f = open('text.txt', 'r')
    mecab_read = f.read()
    f.close()

    tagger = MeCab.Tagger('-Owakati')
    wordlist = tagger.parse(mecab_read)
    wordlist = wordlist.rstrip(' \n').split(' ')

    f = open('l.txt', 'w')
    f.write(str(wordlist))
    f.close()

    markov = {}
    w = ''

    for x in wordlist:
        if w:
            if w in markov:
                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 < 20:
        choice_words = random.choice(wordlist)
        sentence += choice_words
        count += 1
        sentence = sentence.split(' ', 1)[0]
        p = re.compile('[!-/:-@[-`{-~]')
        sus = p.sub('', sentence)

    context = {
        'wordlist': wordlist,
        'sus': sus,
        }

    return render(request,'index.html',context)

index.html

{% extends "base.html" %}
{% block body %}
  <div class="container">
    <div class="row">

      <div class="col-md-12">
        <p class="1">{{ wordlist }}</p>
        <p class="2">{{ sus }}</p>
      </div>

    </div>
  </div>

{% endblock %}

result

I ended up with such a fucking result. There is a lot of money in the description, so you have to remove it ...

I also learned that if you don't cut the actress name, you won't understand it.

I will devote myself.

What I originally wanted to do

I wanted to make an AV title with a Markov chain.