# US sentiment index

## Example code

```python
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