# https://docs.tweepy.org/

# https://towardsdatascience.com/named-entity-recognition-with-nltk-and-spacy-8c4a7d88e7da

# TODO - TRUECASING?

# TODO - FINE TUNE NE MODEL TO NOT CARE ABOUT CASE

# TODO - Model needs to not grab as many words like 'yeah' and 's'

# TODO - Slack integration (ping me, ability to immediately sell, etc.)

# TODO - Can probably auto strip out words he uses all the time like FSD and TESLA and LOOP

# TODO - Add: mike burry, the virgin galactic guy, portnoy

import tweepy

import configparser

import nltk

import sqlite3

import spacy

from spacy import displacy

from collections import Counter

import en_core_web_sm

nlp = en_core_web_sm.load()

from nltk.tokenize import word_tokenize

from nltk.tag import pos_tag

from nltk.chunk import ne_chunk

def preprocess(sent):

return sent

class MyStreamListener(tweepy.StreamListener):

return False

config = configparser.ConfigParser()

config.read('./secrets/config.ini')

twitter = config['twitter']

auth = tweepy.OAuthHandler(twitter['api_key'], twitter['secret_key'])

auth.set_access_token(twitter['access_token'], twitter['access_token_secret'])

api = tweepy.API(auth)

nes = []

# for tweet in api.user_timeline('cnbc'):

# doc = nlp(tweet.text.lower())

# for el in doc:

# print(el)

# print(el.ent_type_)

for i in range(0, 3):

for tweet in api.user_timeline('stoolpresidente', page = i):

for node in preprocess(tweet.text):

if type(node) == nltk.tree.Tree:

for tup in node:

ne = (tup[0])

if ne.lower() == 'first' or ne.lower() == 's' or ne.lower == 'yup' or ne.lower == 'yeah':

continue

nes.append(ne.lower())

print(nes)

symbols = []

con = sqlite3.connect('./data/symbols.db')

cursor = con.cursor()

query = 'SELECT * FROM symbols'

for ne in nes:

for row in cursor.execute(query):

name = row[1]

if ne.lower() in name:

print(ne.lower())

print(name)

symbols.append(row[0])

print(symbols)

cursor.close()

# myStreamListener = MyStreamListener()

# myStream = tweepy.Stream(auth = api.auth, listener=myStreamListener)

# myStream.filter(track=['lebron'])