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TwitterAnalysis.py
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TwitterAnalysis.py
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from cgitb import text
import spacy
from nltk import Tree
from spacy.symbols import *
from Scraper import *
#from nltk.corpus import twitter_samples
#all_positive_tweets = twitter_samples.strings('positive_tweets.json')
#all_negative_tweets = twitter_samples.strings('negative_tweets.json')
test_text="Autonomous cars drives insurance liability toward manufacturers"
nlp = spacy.load("en_core_web_sm")
def search_orgs(text):
doc=nlp(text)
ent_list=[]
for entity in doc.ents:
if entity.label_=="ORG" or "PERSON" or "MONEY":
ent_list.append(entity.text)
return ent_list
def check_org_for_POS_head(text,POS):
ent_list=search_orgs(text)
#print(ent_list)
adjectives=set()
doc=nlp(text)
for token in doc:
#token.text in ent_list and
if token.head.pos == POS and token.text in ent_list:
adjectives.add(token.head)
print(f' ADJ connected to {token.text} are {adjectives}')
#Give points if true.
data=data["title"]
for tweet in data:
check_org_for_POS_head(tweet,VERB)
check_org_for_POS_head(tweet, ADJ)
print (data)
"""
def to_nltk_tree(node,source=None):
if node.n_lefts + node.n_rights > 0:
parsed_child_nodes = [to_nltk_tree(child) for child in node.children]
return Tree(node.orth_, parsed_child_nodes)
else:
return node.orth_
dependency_parser = spacy.load("en_core_web_sm")
my_parsed_sentence = dependency_parser(te)
for sent in my_parsed_sentence.sents:
to_nltk_tree(sent.root).pretty_print()
visited=[]
def dfs(node):
visited.append(node.text)
if not node.children:
return []
for child in node.children:
dfs(node)
print(visited)
"""