afcarl / enrontopicmodelling Goto Github PK
View Code? Open in Web Editor NEWThis project forked from coreylynch/enrontopicmodelling
Topic Modelling the Enron Emails
This project forked from coreylynch/enrontopicmodelling
Topic Modelling the Enron Emails
This uses gensim's online latent dirichlet allocation implementation to topic model the Enron Email dataset. The Enron Emails contain 517,431 emails from 150 Enron executive accounts covering a period from December 1979 through February 2004, with the majority of emails occurring during 1999, 2000, and 2001. After training, we can infer 100 latent topics in the corpus. Topics are interpreted by their probability distribution over words. Example topic word distributions: * topic #34: 0.010*nymex + 0.008*exchange + 0.007*futures + 0.007*member + 0.005*clearing + 0.005*notice * topic #15: 0.011*game + 0.006*fantasy + 0.005*football + 0.005*wr + 0.005*play + 0.005*rb * topic #39: 0.007*emissions + 0.007*environmental + 0.005*etrade + 0.005*epa + 0.005*lcampbensf + 0.004*permit * topic #76: 0.006*chairman + 0.005*president + 0.004*ceo + 0.004*global + 0.004*businesses + 0.004*chief * topic #17: 0.022*sally + 0.013*beck + 0.012*becks + 0.010*sbecknsf + 0.007*sallybeckdec * topic #96: 0.004*agreement + 0.003*credit + 0.003*language + 0.003*such + 0.003*section + 0.003*under * topic #58: 0.006*california + 0.005*market + 0.005*said + 0.004*iso + 0.004*electricity + 0.004*ferc With a trained model we can also infer, for each email, a probability distribution over topics. With each email represented as a vector of topic probabilities, we can do things like cluster similar emails together via the cosine similarity measure, or assign topics to new unseen documents (like user queries) and retrieve all emails that match that topic. Each email gets stored in mongo alongside its dominant topic, it's full distribution, as well as some metadata (e.g. from, to, date, etc.). This allows us to build higher level analysis like the graphs of topic frequency over time. The Enron emails can be obtained here: http://www.cs.cmu.edu/~enron/ Dependencies: * gensim - online (memory-independent) topic modeling. Read more here: http://radimrehurek.com/gensim/ * pymongo * matplotlib for plotting analysis of email topic distributions
A declarative, efficient, and flexible JavaScript library for building user interfaces.
๐ Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. ๐๐๐
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google โค๏ธ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.