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messages_analyzer.py
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import json
import time
import re
import requests
import vk_api
import nltk
import pymorphy2
from nltk.corpus import stopwords
from authorisation import authorisate
import numpy
stopwords = set(stopwords.words('russian') + stopwords.words('english') + [''])
morph = pymorphy2.MorphAnalyzer()
class Analyzer:
def __init__(self):
with open('db/all_messages', 'r') as f:
db = json.load(f)
self.id = str(db['my_id'])
self.name = db['my_name']
self.histories = []
self.vk_session = authorisate()
self.make_mas(db['messages'])
self.statistic()
def make_mas(self, history):
for messages in history:
mess_vec = []
for key_id in messages['history'].keys():
for mess in messages['history'][key_id]:
mess['id'] = key_id
mess_vec.append(mess)
mess_vec.sort(key=lambda a: int(a['date']))
self.histories.append({'name': messages['name'], 'messages': mess_vec})
def get_group_names(self, wall_ids):
vk = self.vk_session.get_api()
res = vk.groups.getById(group_ids=','.join(list(map(str, wall_ids))))
group_names = {}
for group in res:
group_names[group['id']] = group['name']
return group_names
# TODO: слова, присущие только этому человеку
def statistic(self):
# Время
log = str(time.ctime(time.time())) + '\n________________________________________\n\n'
hah = re.compile('(^ах[иа]*)|(^хах*)')
# Диалоги в порядке убывания количества сообщений
self.histories = list(reversed(sorted(self.histories, key=lambda a: len(a['messages']))))
for history in self.histories[:30]:
# Словари
friend = history['name']
feats = {}
for feat_name in ['punctuation', 'dif_words', 'dif_words_norm', 'wall']:
feats[feat_name] = {}
for owner in [self.name, friend]:
feats[feat_name][owner] = {}
for var_name in ['words_num', 'mess_num', 'words_num_10']:
feats[var_name] = {}
for owner in [self.name, friend]:
feats[var_name][owner] = 0
# Прикрепления
attachments_types = ['photo', 'video', 'audio', 'wall', 'sticker']
attachments = dict.fromkeys(attachments_types)
for obj in attachments.keys():
attachments[obj] = {}
attachments[obj][self.name] = 0
attachments[obj][friend] = 0
for mess in history['messages']:
owner = self.name if mess['id'] == self.id else friend
# Мои сообщения
feats['mess_num'][owner] += 1
for word in nltk.tokenize.TweetTokenizer().tokenize(mess['message']):
# Пунктуация
punct = re.sub('\w+', '', word)
if punct != '':
feats['punctuation'][owner][punct] = feats['punctuation'][owner].get(punct, 0) + 1
# Частота слов
feats['words_num'][owner] += 1
word = re.sub('[(\d|\W)]*', '', word).lower()
if word not in stopwords:
feats['dif_words'][owner][word] = feats['dif_words'][owner].get(word, 0) + 1
if len(word) > 10:
feats['words_num_10'][owner] += 1
if mess['attachment'] and mess['attachment']['type'] in attachments.keys():
attachments[mess['attachment']['type']][owner] += 1
if mess['attachment']['type'] == 'wall':
feats['wall'][owner][mess['attachment']['group_id']] = feats['wall'][owner].get(mess['attachment']['group_id'], 0) + 1
# Нормализация слов
for owner in [self.name, friend]:
for word in feats['dif_words'][owner].keys():
norm_word = morph.parse(word)[0].normal_form
if hah.search(norm_word):
norm_word = 'ахах'
feats['dif_words_norm'][owner][norm_word] = feats['dif_words_norm'][owner].get(norm_word, 0) + feats['dif_words'][owner][word]
log += friend + '\n\n'
log += 'Number of messages: ' + str(len(history['messages'])) + '\n\n'
for owner in [self.name, friend]:
log += owner + ': ' + str(feats['mess_num'][owner]) + '\n'
log += '\nNumber of words\n\n'
for owner in [self.name, friend]:
log += owner + ': ' + str(feats['words_num'][owner]) + '\n'
log += '\nNumber of different words\n\n'
for owner in [self.name, friend]:
log += owner + ': ' + str(len(feats['dif_words_norm'][owner].keys())) + '\n'
log += '\nNumber of different words more than 10 symbols length\n\n'
for owner in [self.name, friend]:
log += owner + ': ' + str(feats['words_num_10'][owner]) + '\n'
def add_to_log_freq_words(dic, log):
log = ''
for item, i in zip(reversed(sorted(dic.items(), key=lambda a: a[1])), range(20)):
log += ' '.join(list(map(str, item))) + '\n'
return log
log += '\nMost frequent normal words, except stopwords\n\n'
for owner in [self.name, friend]:
log += owner + ':\n'
log += add_to_log_freq_words(feats['dif_words_norm'][owner], log) + '\n'
log += '\nMost frequent punctuation symbols\n\n'
for owner in [self.name, friend]:
log += owner + ':\n'
log += add_to_log_freq_words(feats['punctuation'][owner], log) + '\n'
# Прикрепления
log += '\nAttacments:\n\n'
for obj in attachments_types:
log += obj + '\n'
for owner in [self.name, friend]:
log += owner + ' ' + str(attachments[obj][owner]) + '\n'
log += '\n'
log += 'Frequent wall\'s reposts\n\n'
for owner in [self.name, friend]:
log += owner + '\n'
groups = []
for item, i in zip(feats['wall'][owner].items(), range(10)):
if item[0] < 0:
groups.append(item)
print(groups)
if groups != []:
groups = numpy.array(list(reversed(sorted(groups, key=lambda a: a[1])))[:10])
print(groups)
group_names = self.get_group_names([str(el)[1:] for el in list(groups[:, 0])])
print(group_names, '\n')
for item in groups:
log += group_names[abs(item[0])] + ' ' + str(item[1]) + '\n'
else:
log += 'No wall\'s attachments\n'
log += '\n'
print('\n\n')
log += '\n________________________________________________________________________\n\n\n'
with open('statistic', 'w') as f:
f.write(log)
if __name__ == '__main__':
Analyzer()