# Keyterm Sentiment Analytics Metrics

## Example code

```python
from finter.data import ContentFactory
cf = ContentFactory("us_stock", 20240101, 20240201)

# sentiment_us category
df = cf.get_df("all-mat_cat_rate", cagegory='sentiment_us')

# sentiment_exp_us category
df = cf.get_df("all-mat_cat_rate", cagegory='sentiment_exp_us')
```

## Metadata

| Valid From | Delivery Schedule  | Time Zone  | Data Frequency |
| ---------- | ------------------ | ---------- | -------------- |
| 2005-01-01 | UTC 00 21 \* \* \* | Asia/Seoul | 1d             |

## Item List

* Use `sentiment_us` category for US
* Use `sentiment_exp_us` category for US Expanded

### All Sentiment

* `all-mat_cat_rate`: Category rate for overall sentiment on the given day in the U.S.
* `all-N_of_mat_cat_each`: Number of news articles for overall sentiment in each category on the given day in the U.S.
* `all-N_of_news`: Number of total news articles for overall sentiment on the given day in the U.S.

### Negative Sentiment

* `neg-mat_cat_rate`: Category rate for negative sentiment on the given day in the U.S.
* `neg-N_of_mat_cat_each`: Number of news articles for negative sentiment in each category on the given day in the U.S.
* `neg-N_of_news`: Number of total news articles for negative sentiment on the given day in the U.S.

### Neutral Sentiment

* `neu-mat_cat_rate`: Category rate for neutral sentiment on the given day in the U.S.
* `neu-N_of_mat_cat_each`: Number of news articles for neutral sentiment in each category on the given day in the U.S.
* `neu-N_of_news`: Number of total news articles for neutral sentiment on the given day in the U.S.

### Positive Sentiment

* `pos-mat_cat_rate`: Category rate for positive sentiment on the given day in the U.S.
* `pos-N_of_mat_cat_each`: Number of news articles for positive sentiment in each category on the given day in the U.S.
* `pos-N_of_news`: Number of total news articles for positive sentiment on the given day in the U.S.


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