{
"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",
"\n",
""
],
"text/plain": [
""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from gensim import corpora, models\n",
"import pyLDAvis.gensim\n",
"\n",
"corpus = corpora.MmCorpus('data/alexip_followers_v3.mm')\n",
"dictionary = corpora.Dictionary.load('data/alexip_followers_v3.dict')\n",
"\n",
"lda = models.LdaModel.load('data/alexip_followers_v3_t40_p200_a001.lda')\n",
"followers_data = pyLDAvis.gensim.prepare(lda,corpus, dictionary)\n",
"pyLDAvis.display(followers_data)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"For Best results set the $\\lambda$ parameter between 0.5 and 0.6. Lowering $\\lambda$ increases the relative importance of words that are discriminant to a certain topic. \n",
"\n",
"We use the amazing [LDAvis](https://pypi.python.org/pypi/pyLDAvis) package for this visualization. LDa was carried out with the [Gensim](https://radimrehurek.com/gensim/) package. The data is available in a [Json 3M gz file](https://github.com/alexperrier/datatalks/raw/master/twitter/data/alexip_followers.json.gz). \n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}