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project.py
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project.py
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import pandas as pd
import math
import spacy
import re
import os
from elasticsearch import Elasticsearch
from index_config import INDEX_DOCUMENTS, INDEX_USERS
from create_index_conf import CREATE_CONF
from collections import Counter
from dotenv import load_dotenv
load_dotenv()
def get_document(id, username, tweet):
return {
'username': username,
'content': tweet['full_text'],
'hashtags': eval(tweet['hashtags']),
'retweet_count' : tweet['retweet_count'],
'favorite_count' : tweet['favorite_count'],
'retweet_count_rf' : math.log2(int(tweet['retweet_count']) + 1) + 0.001,
'favorite_count_rf' : math.log2(int(tweet['favorite_count']) + 1) + 0.001
}
def get_documents(base_path, sources):
print('Preparing documents...')
docs = []
for source in sources.keys():
df = pd.read_csv(base_path + source + '.csv', sep='\t')
for i, tweet in df.iterrows():
doc = get_document(i, source, tweet)
docs.append(doc)
return docs
def tokenize_with_analayzer(es, analyzer, document):
# prepare body object
body = {
"analyzer" : analyzer,
"text" : document
}
# extract tokens objects with analyzer
tokens_raw = es.indices.analyze(body=body, index="documents_index")
tokens = []
# extract filtered tokens
for token_raw in tokens_raw['tokens']:
# if not (token_raw['token'].startswith('symbolat_') or token_raw['token'].startswith('symbolhash_')):
tokens.append(token_raw['token'])
return tokens
def get_top_words(es, analyzer, documents, n=10):
# tokenize
tokens = documents.map(lambda x : tokenize_with_analayzer(es, analyzer, x))
# flatten list
flat_list = [item for sublist in list(tokens.ravel()) for item in sublist]
# get most common words
counter = Counter(flat_list)
top_words = counter.most_common(n)
top_words = list(dict(top_words).keys())
return top_words
def get_top_hashtags(documents, n=10):
# flatten list
flat_list = [item for sublist in list(documents.ravel()) for item in sublist]
# get most common words
counter = Counter(flat_list)
top_words = counter.most_common(n)
top_words = list(dict(top_words).keys())
return top_words
def get_top_entities(documents, n = 10):
nlp = spacy.load("en_core_web_sm")
entities = []
for doc in documents:
# base preprocessing
doc = doc.replace('rt', ' ')
doc = doc.replace('RT', ' ')
doc = doc.replace('#', ' ')
doc = doc.replace('@', ' ')
# doc = re.sub(r'http\S+', '', doc)
doc = re.sub(r'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', '', doc)
tmp = nlp(doc)
for ent in tmp.ents:
if ent.label_ in ['NORP', 'PERSON', 'FAC', 'ORG', 'GPE', 'LOC', 'PRODUCT', 'EVENT']:
ent_text = ent.text.lower()
#ent_text = ent_text.replace('@', '')
entities.append(ent_text)
c = Counter(entities)
most_common = c.most_common(n)
top_entities = list(dict(most_common).keys())
return top_entities
def get_users(es, analyzer, base_path, sources, n_top_words = 7, n_top_hashtags = 3):
# read tweets
df = pd.DataFrame(get_documents(base_path, sources))
# create user profiles
profiles = []
for i, source in enumerate(sources.keys()):
docs_content = df[df['username'] == source]['content']
docs_hashtags = df[df['username'] == source]['hashtags']
top_words = get_top_words(es, analyzer, docs_content, n_top_words)
top_hashtags = get_top_hashtags(docs_hashtags, n_top_hashtags)
top_entities = get_top_entities(docs_content, n_top_words)
row = {
'username': source,
'initials': sources[source],
'top_words': top_words,
'top_entities': top_entities,
'top_hashtags': top_hashtags
}
profiles.append(row)
return profiles
def create_index(es, name, index_conf):
print('Creating index documents...')
if es.indices.exists(name):
es.indices.delete(index=name)
es.indices.create(index=name, body=index_conf)
def insert(es, index, docs):
print('Inserting documents...')
n_documents = len(docs)
for i, doc in enumerate(docs):
print('\r' + '{0}/{1}'.format(i+1,n_documents), end='')
es.index(index=index, id=i, body=doc)
print()
def get_user_info(es, username):
# create query
body = {'query': {
'term': {
'username': username
}
}
}
# search user
results = es.search(index='users_index', size=1, body=body)
return results['hits']['hits'][0]['_source']
def search(es, index, query=None):
print('\nSearch: ')
query = input()
# create query
body = {'query':
{'match':
{
'content': {
"query": query,
"fuzziness": "AUTO"
}
}
}
}
# execute query
execute_query(index=index, body=body, mode='search')
def search_by_popularity(es, index, query=None):
print('\nSearch: ')
query = input()
# create query
body = {'query': {
'script_score': {
'query': {
'match': {
'content': {
'query': query,
'fuzziness': 'AUTO'
}
}
},
'script': {
'source': "_score*(doc['retweet_count'].value + 0.5*doc['favorite_count'].value)"
}
}
}
}
# execute query
execute_query(index=index, body=body, mode='search_by_popularity')
def search_by_popularity_rf(es, index, query=None):
print('\nSearch: ')
query = input()
# create query
body = {'query': {
'bool' : {
'must' : {
'match': {
'content': {
'query': query,
'fuzziness': 'AUTO',
'boost': 0.3
}
}
},
'should' : [
{
'rank_feature': {
'field': 'retweet_count_rf',
'boost': 5.0
}
},
{
'rank_feature': {
'field': 'favorite_count_rf',
'boost': 3.0
}
}
]
}
}
}
# execute query
execute_query(index=index, body=body, mode='search_by_popularity_rf')
def search_content_username(es, index):
print('Search content:')
query_content = input()
print('Search by username:')
query_username = input()
# create query
body = {
'query': {
'bool': {
'filter':
{
'term': {
'username': query_username
}
},
'must': [
{
'match':
{
'content': {
"query": query_content,
"fuzziness": "AUTO"
},
}
}
]
}
}
}
# execute query
execute_query(index=index, body=body, mode='search_content_username')
def search_user_preferences_topwords(es, index):
print('Search content:')
query_content = input()
print('Rank by one of these users ', list(sources.keys()), ':')
username = input()
user = get_user_info(es, username)
top_words = user['top_words']
top_entities = user['top_entities']
top_words = list(set(top_words + top_entities))
# extract top_words
top_words = ' '.join(top_words)
print('TOP WORDS:', top_words, '\n')
# create query
body = {'query': {
'bool': {
'must': [
{'match': {
'content': {
"query": query_content,
"fuzziness": "AUTO"
}
}
}
],
'should': [
{'match': {
'content':
{
'query': top_words,
'boost': 0.6
}
}
},
]
}
}
}
# execute query
execute_query(index=index, body=body, mode='search_user_preferences_topwords')
def search_user_preferences_hashtags(es, index):
print('Search content:')
query_content = input()
print('Rank by one of these users ', list(sources.keys()), ':')
username = input()
user = get_user_info(es, username)
# extract top_hashtags
top_hashtags = user['top_hashtags']
print('TOP HASHTAGS:', top_hashtags, '\n')
# create hashtags ranking, they are ordered by freq descending
should = []
for i, hashtag in enumerate(top_hashtags):
term = {'term': {
'hashtags': {
'value': hashtag,
'boost': len(top_hashtags) - i
}
}
}
should.append(term)
# create query
body = {'query': {
'bool': {
'must': [
{'match': {
'content': {
"query": query_content,
"fuzziness": "AUTO",
'boost': 0.6
}
}
}
],
'should': should
}
}
}
# execute query
execute_query(index=index, body=body, mode='search_user_preferences_hashtags')
def execute_query(index, body, mode=''):
results = es.search(index=index, size=10, body=body)
print('####', mode ,'####')
for hit in results['hits']['hits']:
print('***', hit['_source']['username'], '***')
print(hit['_source']['content'])
print('-RETWEETS:', hit['_source']['retweet_count'])
print('-FAVORITE: ', hit['_source']['favorite_count'])
print('-SCORE: ', hit['_score'])
print('__________')
print('\n\n')
def get_remote_config():
# Parse the auth and host from env:
bonsai = os.environ['BONSAI_URL']
auth = re.search('https\:\/\/(.*)\@', bonsai).group(1).split(':')
host = bonsai.replace('https://%s:%s@' % (auth[0], auth[1]), '')
port=443
es_header = [{
'host': host,
'port': port,
'use_ssl': True,
'http_auth': (auth[0],auth[1])
}]
return es_header
if __name__ == "__main__":
# default local connection
es_header = [{
'host': 'localhost',
'port': 9200,
}]
# get cloud connection if configured
if 'BONSAI_URL' in os.environ:
es_header = get_remote_config()
# instantiate elastic search
es = Elasticsearch(es_header)
# set indexes names
index_documents = 'documents_index'
index_users = 'users_index'
# set path
base_path='./data/'
# define sources
sources = {
'AOC': 'AC',
'BernieSanders': 'BS',
'bgreene': 'BG',
'JoeBiden': 'JB',
'michiokaku': 'MK',
'neiltyson': 'NT'
}
if CREATE_CONF['RECREATE_INDEX_DOCUMENTS']:
# get documents from CSVs
docs = get_documents(base_path=base_path, sources=sources)
# create index
create_index(es, index_documents, INDEX_DOCUMENTS)
# insert documents
insert(es, index_documents, docs)
if CREATE_CONF['RECREATE_INDEX_USERS']:
# create users profiles
users = get_users(es, 'content_analyzer_aux', base_path, sources, n_top_words = 7, n_top_hashtags = 3)
# create index
create_index(es, index_users, INDEX_USERS,)
# insert users
insert(es, index_users, users)
while(True):
print('\nTest functionalities: ')
print('1 - basic search')
print('2 - search and rank by popularity')
print('3 - search personalized by top words')
print('4 - search personalized by top hashtags')
print('0 - stop the program')
action = input()
if action == '1':
search(es, index=index_documents)
elif action == '2':
search_by_popularity_rf(es, index_documents)
elif action == '3':
search_user_preferences_topwords(es, index_documents)
elif action == '4':
search_user_preferences_hashtags(es, index_documents)
else:
print('goodbye!')
break