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I took a sample of the Google Plus graph and loaded it up in to the excellent Gephi graph rendering package. Due to the simple manner in which I crawled the data, this doesn't represent a true breadth-first crawl of the graph (rather it captures the random set of in and out links that appear on a user's profile page). However, what is interesting is that there is a clear tightly connected componen
There were a few of papers at KDD this year on sentiment mining: Sentiment Analysis of Blogs by Combining Lexical Knowledge with Text Classification, Prem Melville, IBM; Wojciech Gryc, ; Richard Lawrence, IBM, USA Entity Discovery and Assignment for Opinion Mining Applications, Xiaowen Ding, Univ of Illinois at Chicago; Bing Liu, UIC; Lei Zhang, UIC OpinionMiner: A Machine Learning System for Web
The workshop that I mentioned briefly in an earlier post now has a CFP. 1st International CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion Measurement (Hong Kong Nov. 6, 2009) News 03-22-2009: Workshop site is up Scope of the Workshop This workshop seeks to bring together researchers in both computer science and social sciences who are interested in developing and using topic-sentiment a
I’m starting to get Twitter as a publication mechanism, and I’ve always understood the value of mining the aggregate of tweets for any number of reasons. However, I don’t totally get Twitter search. In trying to, I’ve been playing around with it a little more. Here’s what I though: Why no link search? Most urls on Twitter go through TinyUrl or something similar, and yet these are not dereferenced.
[updated with Tagommenders paper – thanks Shilad.] The organizers of the World Wide Web conference recently announced the list of accepted papers for this year’s event. In the Social Networks and Web 2.0 track (chaired by Elisa Bertino and Lada Adamic) the following papers are listed (where the paper is available online, I’ve provided a link to the PDF): Sharad Goel, Roby Muhamad and Duncan Watts.
While mining Twitter data for business and marketing intelligence (trend/buzz analysis, sentiment/opinion mining, authority/influence analysis) looks like a compelling path to explore for a business model, it is important to consider the proposition from the point of view of the customer. Enterprises have been working with vendors in this space (mining social media content for BI) for well over 5
This graph shows another view of the core. Rather than require reciprocal links, I have simply pulled out the largest connected component formed by any directional link between blogs. The obvious insight here is the relationship between LiveJournal (blue) and the rest of the core. 28/6/2006, no reciprocal links, partition=1, min weight=1 nodes=52, 952, edges=199, 052 By showing only the links in t
There has been a lot of commentary recently on issues relating to an experimental chat bot that Microsoft has (or had) launched named (after, perhaps, a river in Scotland) Tay. After a brief existence online, the bot was removed due to behaviours perceived as offensive which it was persuaded to engage in. Peter Lee of MSR has this to say about it. While there is much to learn from what transpired,
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