US sentiment index

This dataset includes raw and post-processed SARIMA sentiment analysis metrics from business news articles, along with various US market news tone metrics.

Example code

from finter.data import ContentFactory
cf = ContentFactory("us_stock", 20240101, 20240201)
df = cf.get_df("raw_sentiment_biz_news-1_1-sum_sum")

Metadata

Valid From	Delivery Schedule	Time Zone	Data Frequency
2004-01-01	UTC 0 18 * * *	US/Eastern	1d

Item List

• raw_sentiment_biz_news-1_1-sum_sum: Past processing method, global English sentiment index extracted using sum + sum method

• raw_sentiment_biz_news-1_1-sum_per: Past processing method, global English sentiment index extracted using sum + neutralization method

• raw_sentiment_biz_news-1_1-mean_sum: Past processing method, global English sentiment index extracted using mean + sum method

• raw_sentiment_biz_news-1_1-mean_per: Past processing method, global English sentiment index extracted using mean + neutralization method

• raw_sentiment_biz_news-1_2-sum_sum: Latest processing method, global English sentiment index extracted using sum + sum method

• raw_sentiment_biz_news-1_2-sum_per: Latest processing method, global English sentiment index extracted using sum + neutralization method

• raw_sentiment_biz_news-1_2-mean_sum: Latest processing method, global English sentiment index extracted using mean + sum method

• raw_sentiment_biz_news-1_2-mean_per: Latest processing method, global English sentiment index extracted using mean + neutralization method

• post_sarima_sent_biz_news-1_1-sum_sum: Past processing method, time series processed index of global English sentiment index extracted using sum + sum method

• post_sarima_sent_biz_news-1_1-sum_per: Past processing method, time series processed index of global English sentiment index extracted using sum + neutralization method

• post_sarima_sent_biz_news-1_2-sum_sum: Latest processing method, time series processed index of global English sentiment index extracted using sum + sum method

• post_sarima_sent_biz_news-1_2-sum_per: Latest processing method, time series processed index of global English sentiment index extracted using sum + neutralization method

• us_tone_m3: Sentiment index calculated for positive, negative, and neutral sentiments related to the key term sentiment_us

• us_tone_m3_exp: Sentiment index calculated for positive, negative, and neutral sentiments related to the key term sentiment_exp_us