{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Topic Modeling of Twitter Followers\n", "\n", "This python 2 notebook is a companion to the blog post [Segmentation of Twitter Timelines via Topic Modeling](http://alexperrier.github.io/jekyll/update/2015/09/16/segmentation_twitter_timelines_lda_vs_lsa.html) where we explore a corpus of Twitter timelines composed of the followers of the [@alexip](https://twitter.com/alexip) account and compare the results obtained through Latent Semantic Allocation vs Latent Dirichlet Allocation (LDA). Below are the results for LDA on a set of 245 timelines. \n", "\n", "Some of the best topics are: \n", "\n", "* [T1 Software Development](http://nbviewer.ipython.org/github/alexperrier/datatalks/blob/master/twitter/LDAvis_V2.ipynb#topic=1&lambda=0.57&term=), \n", "* [T2 Data Science](http://nbviewer.ipython.org/github/alexperrier/datatalks/blob/master/twitter/LDAvis_V2.ipynb#topic=2&lambda=0.57&term=), \n", "* [T3 Conference in London](http://nbviewer.ipython.org/github/alexperrier/datatalks/blob/master/twitter/LDAvis_V2.ipynb#topic=2&lambda=0.57&term=), (open for interpretation)\n", "* [T4 Fantasy Football](http://nbviewer.ipython.org/github/alexperrier/datatalks/blob/master/twitter/LDAvis_V2.ipynb#topic=4&lambda=0.57&term=),(mixed with international events)\n", "* [T6 RSS feeds](http://nbviewer.ipython.org/github/alexperrier/datatalks/blob/master/twitter/LDAvis_V2.ipynb#topic=6&lambda=0.57&term=), \n", "* [T8 PMP and Project Management](http://nbviewer.ipython.org/github/alexperrier/datatalks/blob/master/twitter/LDAvis_V2.ipynb#topic=8&lambda=0.5&term=), \n", "* [T19 Martha's Vineyard](http://nbviewer.ipython.org/github/alexperrier/datatalks/blob/master/twitter/LDAvis_V2.ipynb#topic=19&lambda=0.57&term=)\n", "* [T31 Fenway](http://nbviewer.ipython.org/github/alexperrier/datatalks/blob/master/twitter/LDAvis_V2.ipynb#topic=31&lambda=0.57&term=)\n", "* [T33 Addiction and drugs](http://nbviewer.ipython.org/github/alexperrier/datatalks/blob/master/notebooks/twitter/LDAvis_V2.ipynb#topic=33&lambda=0.57&term=)\n", "\n", "etc ...\n", "\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.025\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\n", "
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