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| # Load libaries | |
| import pandas as pd | |
| import numpy as np | |
| from datetime import datetime, timedelta | |
| import cleantext | |
| import re | |
| import ast | |
| import streamlit as st | |
| import spacy | |
| from spacy.lang.en import English | |
| from dotenv import load_dotenv | |
| from subprocess import Popen | |
| import scrapy | |
| from scrapy import Selector | |
| import json | |
| import requests | |
| md_intro = '''# Business News Sentiment Dashboard | |
| The dashboard has 2 tabs: | |
| - Sentiment prediction: receives a news link and outputs sentiment results | |
| - News sentiment report: reports the sentiment of business news from past few days | |
| Main libraries used: scrapy, SpaCy, PyTorch, transformers, streamlit | |
| News scope: CNN, BBC, CNBC (other business news sources don't have free access) | |
| Time scope: up to 3 days (from yesterday to 3 days ago), based on free tier's available data source | |
| ''' | |
| md_sumstats = '''## News Sentiment Summary | |
| ''' | |
| md_table = '''## News Sentiment Report | |
| ''' | |
| md_notes = '''## Notes and Thoughts: | |
| Lexicon-based approach may confuse named entities and actual sentiment, because brand names may have positive words, say BeautifulSoup. | |
| Hence, implementing named entity recognition before sentiment analysis helps to improve accuracy. | |
| Using RNN-based approach can also overcome lexicon issues, but it also takes more resources. | |
| ## References: | |
| https://edition.cnn.com/business | |
| https://www.bbc.com/business | |
| https://www.cnbc.com/business/ | |
| https://huggingface.co/mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis | |
| https://huggingface.co/Venkatesh4342/distilbert-helpdesk-sentence-sentiment | |
| https://kennethenevoldsen.github.io/asent/introduction.html | |
| ''' | |
| dat_name = './news_db/merged_news_data_' + (datetime.today() - timedelta(days=1)).strftime('%Y-%m-%d') + '.csv' | |
| news = pd.read_csv(dat_name, on_bad_lines='skip') | |
| news[['pos_sent', 'neg_sent']] = news[['pos_sent', 'neg_sent']].fillna('') | |
| news['clean_content'] = news.clean_content.apply(lambda x: ast.literal_eval(x)) | |
| news = news.fillna(value = '') | |
| news['clean_date'] = pd.to_datetime(news['clean_date'], dayfirst=True) | |
| # Calculate summary | |
| def news_stats(news, method_selection, range_selection): | |
| overall_sentiment = 0 | |
| news_count = 0 | |
| news = news[news.rnn_arti_score != ''] | |
| news['chosen_score'] = np.where((method_selection == 'Lexicon') | (method_selection is None), | |
| news['arti_score'], news['rnn_arti_score']) | |
| if range_selection == '1 day' or range_selection is None: | |
| overall_sentiment = news[news.date_extracted == (datetime.today() - timedelta(days=1)).strftime('%Y-%m-%d')].chosen_score.mean() | |
| news_count = news[news.date_extracted == (datetime.today() - timedelta(days=1)).strftime('%Y-%m-%d')].title.count() | |
| elif range_selection == '3 days': | |
| overall_sentiment = news.chosen_score.mean() | |
| news_count = news.title.count() | |
| return overall_sentiment, news_count | |
| def news_table(news, date_selection, method_selection): | |
| if date_selection == 'Yesterday' or date_selection is None: | |
| date_selected = (datetime.today() - timedelta(days=1)).strftime('%Y-%m-%d') | |
| elif date_selection == '2 Days Ago': | |
| date_selected = (datetime.today() - timedelta(days=2)).strftime('%Y-%m-%d') | |
| elif date_selection == '3 Days Ago': | |
| date_selected = (datetime.today() - timedelta(days=3)).strftime('%Y-%m-%d') | |
| if method_selection == 'Lexicon' or method_selection is None: | |
| clean_news = news.loc[news.date_extracted == date_selected, ['title', 'arti_score', 'pos_sent', 'neg_sent', 'clean_date', 'url']] | |
| clean_news = clean_news.rename(columns = {'title': 'Title', 'url': 'URL', 'clean_date': 'Date', | |
| 'arti_score': 'Sentiment Score', | |
| 'pos_sent': 'Most Positive Sentence', | |
| 'neg_sent': 'Least Positive Sentence'}) | |
| elif method_selection == 'Transformer': | |
| clean_news = news.loc[news.date_extracted == date_selected, ['title', 'rnn_arti_score', 'rnn_pos_sent', 'rnn_neg_sent', 'clean_date', 'url']] | |
| clean_news = clean_news.rename(columns = {'title': 'Title', 'url': 'URL', 'clean_date': 'Date', | |
| 'rnn_arti_score': 'Sentiment Score', | |
| 'rnn_pos_sent': 'Most Positive Sentence', | |
| 'rnn_neg_sent': 'Least Positive Sentence'}) | |
| # Formatting for table display | |
| clean_news = clean_news.sort_values('Date', ascending = False).reset_index(drop=True) | |
| clean_news.loc[:, 'Title'] = clean_news['Title'].str.wrap(width = 40) | |
| clean_news.loc[:, 'URL'] = clean_news['URL'].str.wrap(width = 65) | |
| clean_news.loc[:, 'Most Positive Sentence'] = clean_news['Most Positive Sentence'].str.wrap(width = 65) | |
| clean_news.loc[:, 'Least Positive Sentence'] = clean_news['Least Positive Sentence'].str.wrap(width = 65) | |
| return clean_news | |
| def bbc_json_extract(bbc_script): | |
| json_data = json.loads(bbc_script) | |
| res = '' | |
| news_key = list(json_data['props']['pageProps']['page'].keys())[0] | |
| for item in json_data['props']['pageProps']['page'][news_key]['contents']: | |
| if item['type'] == 'text': | |
| for block in item['model']['blocks']: | |
| if block['type'] == 'paragraph': | |
| #res = res + ''.join(block['model']['text']).strip() | |
| res = res + block['model']['text'] + ' ' | |
| return res | |
| def link_extract(link): | |
| extracted_content = '' | |
| if newslink is not None and newslink != '': | |
| if 'https://' in newslink or 'http://' in newslink: | |
| clean_link = newslink | |
| else: | |
| clean_link = 'https://' + newslink | |
| html = requests.get(clean_link).content | |
| sel = Selector(text = html) | |
| if 'www.bbc.com' in clean_link: | |
| raw_content = sel.xpath('//body//script//text()').extract()[0] | |
| extracted_content = bbc_json_extract(raw_content) | |
| elif 'edition.cnn.com' in clean_link: | |
| extracted_content = ''.join(sel.xpath('//p[contains(@class, "paragraph") and not(@class="footer__disclaimer-text") and not(@class="footer__copyright-text")]//text()').getall()).strip() | |
| elif 'www.cnbc.com' in clean_link: | |
| extracted_content = ''.join(sel.xpath('//div[@class="ArticleBody-articleBody"]//p//text()').getall()).strip() | |
| return extracted_content | |
| def sentence_breakdown(string): | |
| # Transform scraped data to a list of separate sentences | |
| sentences = "" | |
| if string != "": | |
| clean_string = cleantext.clean(string, extra_spaces = True) | |
| for ch in [r'\r', r'\n', r'\t', r'\xa0', '\r', '\n', '\t', '\xa0']: | |
| if ch in clean_string: | |
| clean_string = clean_string.replace(ch, '') | |
| nlp = English() | |
| nlp.add_pipe('sentencizer') | |
| doc = nlp(clean_string) | |
| sentences = [sent.text.strip() for sent in doc.sents] | |
| return sentences | |
| def rnn_sentence_sentiment(sent): | |
| from transformers import TextClassificationPipeline, AutoTokenizer, AutoModelForSequenceClassification | |
| tokenizer = AutoTokenizer.from_pretrained("Venkatesh4342/distilbert-helpdesk-sentence-sentiment") | |
| model = AutoModelForSequenceClassification.from_pretrained("Venkatesh4342/distilbert-helpdesk-sentence-sentiment") | |
| pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer) | |
| prediction = pipe(sent, top_k=1) | |
| # assign score to sentiment, positive sentiment has positive score, negative sentiment has negative score, neutral has 0 | |
| if prediction[0]['label'] == 'Positive': | |
| res = prediction[0]['score'] | |
| elif prediction[0]['label'] == 'Negative': | |
| res = -prediction[0]['score'] | |
| elif prediction[0]['label'] == 'Neutral': | |
| res = 0 | |
| return res | |
| def article_sentiment(arti): | |
| # Input is a list of strings/sentences | |
| scores = dict() | |
| for sent in arti: | |
| scores[sent] = rnn_sentence_sentiment(sent) | |
| scores_list = list(scores.values()) | |
| arti_score = np.mean([score for score in scores_list if score != 0]) | |
| pos_sents = sorted(scores, key=scores.get, reverse=True)[:3] | |
| neg_sents = sorted(scores, key=scores.get, reverse=False)[:3] | |
| return round(arti_score, 3), pos_sents, neg_sents | |
| st.markdown(md_intro) | |
| tab_pred, tab_news = st.tabs(["Sentiment Prediction", "News Report"]) | |
| with tab_pred: | |
| st.write("This is a sentiment prediction module.\nPlease enter your news link into the textbox.\nSentiment prediction will be returned shortly!.\nExample link: https://www.bbc.com/news/technology-68818113") | |
| newslink = st.chat_input(placeholder="Please input CNN/BBC/CNBC link") | |
| if newslink: | |
| placeholder = st.empty() | |
| placeholder.text("Running ...") | |
| extracted_content = link_extract(newslink) | |
| cleaned_content = sentence_breakdown(extracted_content) | |
| arti_score, user_pos_sents, user_neg_sents = article_sentiment(cleaned_content) | |
| placeholder.empty() | |
| if newslink: | |
| st.markdown(f'### Article sentiment score is: {arti_score}') | |
| st.markdown("### Three most positive sentences are: ") | |
| st.markdown(f"{user_pos_sents[0]}") | |
| st.markdown(''' | |
| ''') | |
| st.markdown(f'''{user_pos_sents[1]} | |
| ''') | |
| st.markdown(f'''{user_pos_sents[2]} | |
| ''') | |
| st.markdown("### Three most negative sentences are: ") | |
| st.markdown(f'''{user_neg_sents[0]} | |
| ''') | |
| st.markdown(f'''{user_neg_sents[1]} | |
| ''') | |
| st.markdown(f"{user_neg_sents[2]}") | |
| with tab_news: | |
| st.markdown(md_sumstats) | |
| method_col, range_col = st.columns(2) | |
| with method_col: | |
| method_selection = st.selectbox("Select Method", ('Lexicon', 'Transformer')) | |
| with range_col: | |
| range_selection = st.selectbox("Statistics Range", ('1 day', '3 days')) | |
| overall_sentiment, news_count = news_stats(news, method_selection, range_selection) | |
| senti_col, count_col = st.columns(2) | |
| senti_col.metric("Overall Sentiment", str(round(overall_sentiment, 3))) | |
| count_col.metric("Number of News", str(news_count)) | |
| st.markdown(md_table) | |
| date_selection = st.selectbox("Extraction Date", ('Yesterday', '2 Days Ago', '3 Days Ago')) | |
| clean_news = news_table(news, date_selection, method_selection) | |
| st.dataframe(data=clean_news, | |
| column_config={"Title": st.column_config.Column(width=250), | |
| "Most Positive Sentence": st.column_config.Column(width=400), | |
| "Least Positive Sentence": st.column_config.Column(width=400), | |
| "Date": st.column_config.DateColumn(format="DD-MM-YYYY"), | |
| "URL": st.column_config.LinkColumn("App URL", width=400) | |
| }) | |
| st.markdown(md_notes) |