PUMA publications for /user/jaeschke/onlinehttps://puma.uni-kassel.de/user/jaeschke/onlinePUMA RSS feed for /user/jaeschke/online2024-03-29T11:56:26+01:00Using Crowdsourcing and Active Learning to Track Sentiment in Online Mediahttps://puma.uni-kassel.de/bibtex/29643e3c5729886b0b4e85cb3d3d704f5/jaeschkejaeschke2012-09-18T18:03:31+02:00active analysis crowdsourcing datamining learning media online sentiment web <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Anthony Brew" itemprop="url" href="/author/Anthony%20Brew"><span itemprop="name">A. Brew</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Derek Greene" itemprop="url" href="/author/Derek%20Greene"><span itemprop="name">D. Greene</span></a></span>, и <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Pádraig Cunningham" itemprop="url" href="/author/P%c3%a1draig%20Cunningham"><span itemprop="name">P. Cunningham</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 19th European Conference on Artificial Intelligence</span>, </em></span><em>том 215 из Frontiers in Artificial Intelligence and Applications, </em><em>стр. <span itemprop="pagination">145--150</span>. </em><em>Amsterdam, The Netherlands, The Netherlands, </em><em><span itemprop="publisher">IOS Press</span>, </em>(<em><span>2010<meta content="2010" itemprop="datePublished"/></span></em>)Tue Sep 18 18:03:31 CEST 2012Amsterdam, The Netherlands, The NetherlandsProceedings of the 19th European Conference on Artificial Intelligence145--150Frontiers in Artificial Intelligence and ApplicationsUsing Crowdsourcing and Active Learning to Track Sentiment in Online Media2152010active analysis crowdsourcing datamining learning media online sentiment web Tracking sentiment in the popular media has long been of interest to media analysts and pundits. With the availability of news content via online syndicated feeds, it is now possible to automate some aspects of this process. There is also great potential to crowdsource Crowdsourcing is a term, sometimes associated with Web 2.0 technologies, that describes outsourcing of tasks to a large often anonymous community. much of the annotation work that is required to train a machine learning system to perform sentiment scoring. We describe such a system for tracking economic sentiment in online media that has been deployed since August 2009. It uses annotations provided by a cohort of non-expert annotators to train a learning system to classify a large body of news items. We report on the design challenges addressed in managing the effort of the annotators and in making annotation an interesting experience.Online Controlled Experiments: Introduction, Learnings, and Humbling Statisticshttps://puma.uni-kassel.de/bibtex/2aa31e13651d5d1eab42e449e55a0e745/jaeschkejaeschke2012-09-20T09:59:05+02:002012 amazon bing evaluation experiment industry keynote online recommender recsys statistics <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ron Kohavi" itemprop="url" href="/author/Ron%20Kohavi"><span itemprop="name">R. Kohavi</span></a></span>. </span>(<em><span>12.09.2012<meta content="12.09.2012" itemprop="datePublished"/></span></em>)Thu Sep 20 09:59:05 CEST 2012sepOnline Controlled Experiments: Introduction, Learnings, and Humbling StatisticsIndustry keynote at ACM Recommender Systems20122012 amazon bing evaluation experiment industry keynote online recommender recsys statistics 12The web provides an unprecedented opportunity to accelerate innovation by evaluating ideas quickly and accurately using controlled experiments (e.g., A/B tests and their generalizations). Whether for front-end user-interface changes, or backend recommendation systems and relevance algorithms, online controlled experiments are now utilized to make data-driven decisions at Amazon, Microsoft, eBay, Facebook, Google, Yahoo, Zynga, and at many other companies. While the theory of a controlled experiment is simple, and dates back to Sir Ronald A. Fisher’s experiments at the Rothamsted Agricultural Experimental Station in England in the 1920s, the deployment and mining of online controlled experiments at scale—thousands of experiments now—has taught us many lessons. We provide an introduction, share real examples, key learnings, cultural challenges, and humbling statistics. Analysis of topological characteristics of huge online social networking serviceshttps://puma.uni-kassel.de/bibtex/280928579cc079e0e27c8a28b23a300b7/jaeschkejaeschke2009-06-10T10:17:59+02:00folksonomy online analysis network sna social <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Yong-Yeol Ahn" itemprop="url" href="/author/Yong-Yeol%20Ahn"><span itemprop="name">Y. Ahn</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Seungyeop Han" itemprop="url" href="/author/Seungyeop%20Han"><span itemprop="name">S. Han</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Haewoon Kwak" itemprop="url" href="/author/Haewoon%20Kwak"><span itemprop="name">H. Kwak</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Sue Moon" itemprop="url" href="/author/Sue%20Moon"><span itemprop="name">S. Moon</span></a></span>, и <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Hawoong Jeong" itemprop="url" href="/author/Hawoong%20Jeong"><span itemprop="name">H. Jeong</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 16th International Conference on World Wide Web</span>, </em></span><em>стр. <span itemprop="pagination">835--844</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2007<meta content="2007" itemprop="datePublished"/></span></em>)Wed Jun 10 10:17:59 CEST 2009New York, NY, USAProceedings of the 16th International Conference on World Wide Web835--844Analysis of topological characteristics of huge online social networking services2007folksonomy online analysis network sna social Social networking services are a fast-growing business in the Internet. However, it is unknown if online relationships and their growth patterns are the same as in real-life social networks. In this paper, we compare the structures of three online social networking services: Cyworld, MySpace, and orkut, each with more than 10 million users, respectively. We have access to complete data of Cyworld's ilchon (friend) relationships and analyze its degree distribution, clustering property, degree correlation, and evolution over time. We also use Cyworld data to evaluate the validity of snowball sampling method, which we use to crawl and obtain partial network topologies of MySpace and orkut. Cyworld, the oldest of the three, demonstrates a changing scaling behavior over time in degree distribution. The latest Cyworld data's degree distribution exhibits a multi-scaling behavior, while those of MySpace and orkut have simple scaling behaviors with different exponents. Very interestingly, each of the two e ponents corresponds to the different segments in Cyworld's degree distribution. Certain online social networking services encourage online activities that cannot be easily copied in real life; we show that they deviate from close-knit online social networks which show a similar degree correlation pattern to real-life social networks.