exact_data2023/application/functions/sentiment.py

from transformers import pipeline
import re
from facebook_scraper import get_posts

pipe = pipeline('text-classification', model="Scigi/sentiment-analysis-model", tokenizer = "Scigi/sentiment-analysis-model")

def sentiment_prediction(data):
    result = pipe(data)
    
    return result

def clear_data(data):
    data = [re.sub(r"[^A-Za-zżźćńółęąśŻŹĆĄŚĘŁÓŃ ']+", r"", i) for i in data['sentences']]
    data = [x for x in data if x != '']
    data = [i.strip() for i in data]
    data = [i.lower() for i in data]

    return data

def count_predictions(predictions):
    l0 = 0
    l1 = 0
    l2 = 0
    all = {}

    for i in predictions:
        if i['label'] == 'LABEL_0':
            l0 += 1
        if i['label'] == 'LABEL_1':
            l1 += 1
        if i['label'] == 'LABEL_2':
            l2 += 1

    all['positive'] = l1
    all['negative'] = l0
    all['neutral'] = l2

    return all

def scrapp_comments(url):
    comments= []
    all = {}
    for post in get_posts(post_urls=[url], cookies = 'cookies.txt', options={"allow_extra_requests": False, "comments":True, "extra_info":True}):
        text_post = post['text']
        for comment in post['comments_full']:
            comments.append(comment['comment_text'])
    all['post'] = text_post
    all['sentences'] = comments

    return all