File size: 1,590 Bytes
c302b1e b06e000 c302b1e afa6b01 c302b1e 2e4daec c302b1e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 |
from transformers import AutoTokenizer, AutoConfig, AutoModelForSequenceClassification
from scipy.special import softmax
import emoji
# Define the preprocess function
def preprocess(text):
new_text = []
for t in text.split(" "):
# Check if the token is an emoji
if emoji.is_emoji(t):
# Add the emoji to the new text list
new_text.append(t)
else:
# Check if the token starts with '@' and has more than one character
if t.startswith('@') and len(t) > 1:
# Replace the token with '@user'
t = '@user'
# Check if the token starts with 'http'
if t.startswith('http'):
# Replace the token with 'http'
t = 'http'
# Add the processed token to the new text list
new_text.append(t)
return " ".join(new_text)
# Define the sentiment_analysis function
def sentiment_analysis(text, tokenizer, model):
text = preprocess(text)
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
scores_ = output[0][0].detach().numpy()
scores_ = softmax(scores_)
labels = ['Negative', 'Positive']
scores = {l: float(s) for (l, s) in zip(labels, scores_)}
return scores
# Define the map_sentiment_score_to_rating function
def map_sentiment_score_to_rating(score):
min_score = 0.0
max_score = 1.0
min_rating = 1
max_rating = 10
rating = ((score - min_score) / (max_score - min_score)) * (max_rating - min_rating) + min_rating
return rating |