You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
18 lines
1.6 KiB
18 lines
1.6 KiB
# Poetic license
|
|
|
|
## Instructions
|
|
|
|
For [dis notebook](https://www.kaggle.com/jenlooper/emily-dickinson-word-frequency), you go see more than 500 Emily Dickinson poems wey dem don already analyze for sentiment using Azure text analytics. Use dis dataset take analyze am with di techniques wey dem describe for di lesson. Di sentiment wey dem suggest for one poem, e match di decision wey di more advanced Azure service make? Why or why e no match, for your own opinion? Anything surprise you?
|
|
|
|
## Rubric
|
|
|
|
| Criteria | Exemplary | Adequate | Needs Improvement |
|
|
| -------- | -------------------------------------------------------------------------- | ------------------------------------------------------- | ------------------------ |
|
|
| | Notebook dey show correct analysis of di author sample output | Notebook no complete or e no do di analysis well | No notebook dey show |
|
|
|
|
---
|
|
|
|
<!-- CO-OP TRANSLATOR DISCLAIMER START -->
|
|
**Disclaimer**:
|
|
Dis dokyument don translate wit AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). Even as we dey try make am accurate, abeg make you sabi say machine translation fit get mistake or no dey correct well. Di original dokyument wey dey for im native language na di main source wey you go trust. For important mata, e good make professional human translator check am. We no go fit take blame for any misunderstanding or wrong interpretation wey fit happen because you use dis translation.
|
|
<!-- CO-OP TRANSLATOR DISCLAIMER END --> |