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sitc/nlp/4_7_Exercises.ipynb
2020-04-29 18:46:31 +02:00

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"![](images/EscUpmPolit_p.gif \"UPM\")"
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"metadata": {},
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"# Course Notes for Learning Intelligent Systems"
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"Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © Carlos A. Iglesias"
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"# Exercises"
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"# Table of Contents\n",
"\n",
"* [Exercises](#Exercises)\n",
"\t* [Exercise 1 - Sentiment Analysis on Movie Reviews](#Exercise-1---Sentiment-Analysis-on-Movie-Reviews)\n",
"\t* [Exercise 2 - Spam classification](#Exercise-2---Spam-classification)\n",
"\t* [Exercise 3 - Automatic essay classification](#Exercise-3---Automatic-essay-classification)"
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{
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"source": [
"# Exercises"
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"Here we propose several exercises, it is recommended to work only in one of them."
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"## Exercise 1 - Sentiment Analysis on Movie Reviews"
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"You can try the exercise Exercise 2: Sentiment Analysis on movie reviews of Scikit-Learn https://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html. \n",
"Previously you should follow the installation instructions in the section Tutorial Setup."
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"## Exercise 2 - Spam classification"
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"The classification of spam is a classical problem. [Here](http://zacstewart.com/2015/04/28/document-classification-with-scikit-learn.html) you can find a detailed example of how to do it using the datasets Enron-Spama and SpamAssassin. You can try to test yourself the classification."
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"## Exercise 3 - Automatic essay classification"
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"As you have seen, we did not got great results in the previous notebook. You can try to improve them."
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{
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"source": [
"## Licence"
]
},
{
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"source": [
"The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n",
"\n",
"© Carlos A. Iglesias, Universidad Politécnica de Madrid."
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