# Poetic license ## Instructions In [this notebook](https://www.kaggle.com/jenlooper/emily-dickinson-word-frequency), you will find over 500 poems by Emily Dickinson that have been previously analyzed for sentiment using Azure text analytics. Using this dataset, apply the techniques discussed in the lesson to conduct your own analysis. Does the sentiment suggested by a poem align with the decision made by the more advanced Azure service? Why or why not, in your opinion? Did anything surprise you during the analysis? ## Rubric | Criteria | Exemplary | Adequate | Needs Improvement | | -------- | -------------------------------------------------------------------------- | ------------------------------------------------------- | ------------------------ | | | A notebook is presented with a thorough analysis of sample outputs from the author | The notebook is incomplete or lacks proper analysis | No notebook is presented | --- **Disclaimer**: This document has been translated using the AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). While we strive for accuracy, please note that automated translations may contain errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is recommended. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation.