After a brief incursion into LDA, it appeared to me that visualization of topics and of its components played a major role in interpreting the model. In this blog post I will write about my experience with PyLDAvis, a python package (ported from R) that allows an interactive visualization of a topic model.
Continue reading “Experiments on Topic Modeling – PyLDAvis”
Topic modeling is an approach or a method through which a collection is organized/structured/labeled according to themes found in its contents. Continue reading “Experiments on Topic Modeling – LDA”
The number of patents in which Facebook is the assignee currently surpasses the 5000. Not all these patents are closely related to our research, certainly – but how to make a good selection amongst such a huge amount of documents? The interface of the service we chose to use made it possible to use filters which certainly help ‘optimize’ the number of results. By using specific search terms, the displayed results are tailored to what one knows to be relevant. But what about the words that one does not know that could also be relevant? How can one have an overview of the existing content while selecting relevant pieces from it? This blog post is a brief overview of how we approached this issue. Continue reading “Mining patent data – preliminary results”
Our mapping strategy relies on understanding the processes employed by Facebook to make inferences about their users. One possible way to accomplish that is by having a look at the patents they published. Continue reading “Mining patent data”
How to start a research that aims at identifying and describing Facebook’s strategies in mapping our desires?
When dealing with such a huge network of data, tools, categories and involved parties, it does not take much to get lost. Our preliminary intuition was to have a look at and register the most basic entry points: the tools that Facebook makes available for advertisers and developers. Continue reading “Mapping the Network”