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Ruben van de Ven 2023-12-30 16:00:03 +01:00
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Mapping Movements.ipynb
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@ -14,12 +14,26 @@
"Many thanks to the exploration of Doc2Vec done by [Marton Trencseni](https://bytepawn.com/similar-posts-recommendation-with-doc2vec.html), on which many of these aproaches are based." "Many thanks to the exploration of Doc2Vec done by [Marton Trencseni](https://bytepawn.com/similar-posts-recommendation-with-doc2vec.html), on which many of these aproaches are based."
] ]
}, },
{
"cell_type": "markdown",
"id": "af8da066",
"metadata": {},
"source": [
"Contents: \n",
"\n",
"- [Embedding the text](#embedding)\n",
"- [Similar initiatives](#similar)\n",
"- [Find Clusters](#clusters)\n",
"- [Alternative Maps: Rendering Embedding Space](#rendering)\n",
"- [A situated map](#map)\n"
]
},
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "aaac4f97", "id": "aaac4f97",
"metadata": {}, "metadata": {},
"source": [ "source": [
"## 1. Embedding the text\n", "## 1. Embedding the text <a name=\"embedding\"></a>\n",
"\n", "\n",
"In this first step, the descriptions of the initiatives are embedded using Doc2Vec. That is to say, in a series of steps the text of the descriptions is transformed into a vector, a point in a (latent) multidimensional space: an embedding." "In this first step, the descriptions of the initiatives are embedded using Doc2Vec. That is to say, in a series of steps the text of the descriptions is transformed into a vector, a point in a (latent) multidimensional space: an embedding."
] ]
@ -170,7 +184,7 @@
"id": "2e6b9b9d", "id": "2e6b9b9d",
"metadata": {}, "metadata": {},
"source": [ "source": [
"## 2. Similar initiatives\n", "## 2. Similar initiatives <a name=\"similar\"></a>\n",
"\n", "\n",
"Now that we have a Doc2Vec model, we can use it to calculate some sort of similarity score between the initiative in our little dataset which is based on a mathematical similarity of words ocuring in their descriptions. " "Now that we have a Doc2Vec model, we can use it to calculate some sort of similarity score between the initiative in our little dataset which is based on a mathematical similarity of words ocuring in their descriptions. "
] ]
@ -289,17 +303,9 @@
"id": "562d4340-e4b0-4366-a917-ba91056c8ced", "id": "562d4340-e4b0-4366-a917-ba91056c8ced",
"metadata": {}, "metadata": {},
"source": [ "source": [
"## 3. Find clusters" "## 3. Find clusters <a name=\"clusters\"></a>"
] ]
}, },
{
"cell_type": "code",
"execution_count": null,
"id": "256a6c0a",
"metadata": {},
"outputs": [],
"source": []
},
{ {
"cell_type": "markdown", "cell_type": "markdown",
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@ -771,7 +777,7 @@
"id": "fa10d87a-4435-4e8c-bde1-96861d4401ad", "id": "fa10d87a-4435-4e8c-bde1-96861d4401ad",
"metadata": {}, "metadata": {},
"source": [ "source": [
"## 4. Rendering embedding space: alternative maps of movement" "## 4. Rendering embedding space: alternative maps of movement <a name=\"rendering\"></a>"
] ]
}, },
{ {
@ -888,7 +894,7 @@
"id": "70b3afe8", "id": "70b3afe8",
"metadata": {}, "metadata": {},
"source": [ "source": [
"## 5. A situated map" "## 5. A situated map <a name=\"map\"></a>"
] ]
}, },
{ {