51 lines
1.3 KiB
Python
51 lines
1.3 KiB
Python
from transformers import pipeline
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import re
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from facebook_scraper import get_posts
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pipe = pipeline('text-classification', model="Scigi/sentiment-analysis-model", tokenizer = "Scigi/sentiment-analysis-model")
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def sentiment_prediction(data):
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result = pipe(data)
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return result
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def clear_data(data):
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data = [re.sub(r"[^A-Za-zżźćńółęąśŻŹĆĄŚĘŁÓŃ ']+", r"", i) for i in data['sentences']]
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data = [x for x in data if x != '']
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data = [i.strip() for i in data]
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data = [i.lower() for i in data]
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return data
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def count_predictions(predictions):
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l0 = 0
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l1 = 0
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l2 = 0
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all = {}
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for i in predictions:
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if i['label'] == 'LABEL_0':
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l0 += 1
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if i['label'] == 'LABEL_1':
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l1 += 1
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if i['label'] == 'LABEL_2':
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l2 += 1
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all['positive'] = l1
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all['negative'] = l0
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all['neutral'] = l2
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return all
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def scrapp_comments(url):
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comments= []
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all = {}
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for post in get_posts(post_urls=[url], options={"allow_extra_requests": False, "comments":True, "extra_info":True}):
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text_post = post['text']
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for comment in post['comments_full']:
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comments.append(comment['comment_text'])
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all['post'] = text_post
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all['sentences'] = comments
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return all
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