nlp/4_7_Exercises.ipynb

@@ -35,7 +35,7 @@
     "# 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 1 - Sentiment classification for Twitter](#Exercise-1---Sentiment-classification-for-Twitter)\n",
     "\t* [Exercise 2 - Spam classification](#Exercise-2---Spam-classification)\n",
     "\t* [Exercise 3 - Automatic essay classification](#Exercise-3---Automatic-essay-classification)"
    ]
@@ -58,15 +58,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Exercise 1 - Sentiment Analysis on Movie Reviews"
+    "## Exercise 1 - Sentiment classification for Twitter"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "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.htmlt\n",
-    "* Previously you should follow the installation instructions in the section 'Tutorial Setup'",
+    "The purpose of this exercise is:\n",
+    "* Collect geolocated tweets\n",
+    "* Analyse their sentiment\n",
+    "* Represent the result in a map, so that one can understand the sentiment in a geographic region.\n",
+    "\n",
+    "The steps (and most of the code) can be found [here](http://pybonacci.org/2015/11/24/como-hacer-analisis-de-sentimiento-en-espanol-2/). \n",
+    "\n",
+    "You can select the tweets in any language."
    ]
   },
   {