Topic Modeling and Twitter

Alex Perrier has two recent posts of interest to Twitter users and topic modelers:

Topic Modeling of Twitter Followers

In this post, we explore LDA an unsupervised topic modeling method in the context of twitter timelines. Given a twitter account, is it possible to find out what subjects its followers are tweeting about?

Knowing the evolution or the segmentation of an account’s followers can give actionable insights to a marketing department into near real time concerns of existing or potential customers. Carrying topic analysis of followers of politicians can produce a complementary view of opinion polls.

Segmentation of Twitter Timelines via Topic Modeling

Following up on our first post on the subject, Topic Modeling of Twitter Followers, we compare different unsupervised methods to further analyze the timelines of the followers of the @alexip account. We compare the results obtained through Latent Semantic Analysis and Latent Dirichlet Allocation and we segment Twitter timelines based on the inferred topics. We find the optimal number of clusters using silhouette scoring.

Alex has Python code, an interesting topic, great suggestions for additional reading, what is there not to like?

LDA, machine learning types follow @alexip but privacy advocates should as well.

Consider this recent tweet by Alex:

In the end the best way to protect your privacy is to behave erratically so that the Machine Learning algo will detect you as an outlier!

Perhaps, perhaps, but I suspect outliers/outsiders are classed as dangerous by several government agencies in the US.

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