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from peft import AutoPeftModelForCausalLM
import spacy
import pandas as pd
from textblob import TextBlob
def load_model():
nlp = spacy.load("en_core_web_sm")
return nlp
def extract_entities(text,nlp):
doc = nlp(text)
entities = [(ent.text, ent.label_) for ent in doc.ents]
return entities
# Function to extract entities context
def extract_entities_with_context(text, nlp, window=5):
doc = nlp(text)
entity_context = []
for ent in doc.ents:
start = max(0, ent.start - window)
end = min(len(doc), ent.end + window)
context = doc[start:end].text
entity_context.append((ent.text, ent.label_, context))
return entity_context
def get_sentiment(text):
return TextBlob(text).sentiment.polarity
def analyze_entity_sentiments(entity_contexts):
sentiments = []
for text, label, context in entity_contexts:
sentiment = get_sentiment(context)
sentiments.append((text, label, sentiment))
return sentiments
def analyze_entity_sentiments_score(entity_contexts):
sentiments = []
for text, label, context in entity_contexts:
sentiment = get_sentiment(context)
sentiments.append((sentiment))
return sentiments
def calculate_avg_score(scores):
if scores:
return sum(scores) / len(scores)
else:
return float('inf')
def categorize_sentiment(score):
if score <= -0.1:
return 'Negative'
elif score >= 0.1:
return 'Positive'
else:
return 'Neutral' |