Archives of #Tweet

Some Natural Language Processing and ML Papers

After I spoke at DataScienceLondon in June I was given a set of paper references by a couple of people (the bulk were by Levente Török) – thanks to all. They’re listed below. Along the same lines I have one machine learning paper aimed at beginners to recommend (“A Few Useful Things to Know about […]

Overfitting with a Decision Tree

Below is a plot of Training versus Testing errors using a Precision metric (actually 1.0-precision, so lower is better) that shows how easy it is to over-fit a decision tree to the detriment of generalisation. It is important to check that a classifier isn’t overfitting to the training data such that it is just learning […]

Visualising the internals of Logistic Regression on a Text Matrix

Below I have some plots that visualise the term matrix (as a binary matrix and as a TF-IDF matrix) for the brand disambiguation project followed by a visualisation of the coefficients used in scikit-learn’s LogisticRegression classifier using l1 and l2 penalties. Using a CountVectorizer with binary=True we can mark the absence or presence of a […]

Social Media Brand Disambiguator first steps

As noted a few days back I’m spending June working on a social-media focused brand disambiguator using Python, NLTK and scikit-learn. This project has grown out of frustrations using existing Named Entity Recognition tools (like OpenCalais and DBPediaSpotlight) to recognise brands in social media messages. These tools are generally trained to work on long-form clean […]

June project: Disambiguating “brands” in Social Media

Having returned from Chile last year, settled in to consulting in London, got married and now on honeymoon I’m planning on a change for June. I’m taking the month off from clients to work on my own project, an open sourced brand disambiguator for social media. As an example this will detect that the following […]

Semantic map of PyCon2013 Twitter Topics

Maksim taught a lovely Social Graph Analytics course at PyCon the day before I taught Applied Parallel Computing. I took his demo for a “poor mans LDA/LSI analysis” of a Twitter topic (rather than using full LDA it just uses co-incident hashtags) and added usernames to produce the plot below. Update – Analysing #pydata conference […]

ANN: twitter-text-python release (Python Tweet parsing library)

A few weeks back I took over as maintainer of the twitter-text-python library (source on github). This library lets you take a tweet like: "@ianozsvald, you now support #IvoWertzel's tweet ... parser!" and extract the Twitter entities as defined in the Twitter conformance tests. The entities in the above tweet would be: reply: 'ianozsvald' […]

Layers of “data science”?

The field of “data science” covers a lot of areas, it feels like there’s a continuum of layers that can be considered and lumping them all as “data science” is perhaps less helpful than it could be. Maybe by sharing my list you can help me with further insight. In terms of unlocking value in […]

Map/Reduce (Disco) on millions of tweets

Whilst working on data sciencey problems for AdaptiveLab I’m becoming more involved in simple visualisations for proof-of-concepts for clients. This ties in nicely with my PyCon Parallel Computing tutorial with Minesh. I’ve been prototyping a Disco map/reduce tutorial (part 2 for PyCon) using tweets collected during the life of SocialTies during 2011-2012. Using 11,645,331 tweets […]
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