<jupyter_start><jupyter_text>Data Exploration News DataThis notebook will explore data sources for news data (especially short news ticker) for information mining using NLP and sentiment analysis.<jupyter_code>import feedparser
import pandas as pd
d = feedparser.parse('https://www.ft.com/business-education?format=rss')
feed = pd.DataFrame(d['entries'])
meta = d['feed']
feed.head()
d = feedparser.parse('http://rss.cnn.com/rss/money_news_economy.rss')
feed = pd.DataFrame(d['entries'])
meta = d['feed']
feed.head()<jupyter_output><empty_output><jupyter_text>Data ParsingThis part outlines the ETL pipeline for the data to use for sentiment analysis<jupyter_code># TODO<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.11857142857142858, 'fraction_numerical': 0.0, 'mean_word_length': 7.345238095238095, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 1, 'lorem ipsum': 0, 'www.': 1, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '665257', 'license': 'MIT', 'n_tokens_mistral': 225, 'n_tokens_neox': 220, 'n_words': 58, 'path': 'notebooks/01-3_news-data-exploration.ipynb', 'repo_name': 'stungkit/stock_trend_analysis', 'size': '700'}