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”
After going through the process of mining and selecting relevant patents (you can read all about it here), and ending up with 530 “Must Read” patents and 254 “Maybe” patents, I started the process of reading and summarizing them. Continue reading “Schematic Patents”
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”
Before Lucia and me decided to take a more systematic and scalable approach towards the selection of relevant patents, I performed a manual, preliminary search at Fresh Patents which yielded around 100 results containing certain important keywords in the title (e.g.: categorizing, targeting, clustering, etc). From this preliminary selection, I read three patents: Continue reading “A Tale of 3 Patents”
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”