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Full-Text Articles in Science and Technology Studies
Active Learning With Efficient Feature Weighting Methods For Improving Data Quality And Classification Accuracy, Justin Martineau, Lu Chen, Doreen Cheng, Amit P. Sheth
Active Learning With Efficient Feature Weighting Methods For Improving Data Quality And Classification Accuracy, Justin Martineau, Lu Chen, Doreen Cheng, Amit P. Sheth
Kno.e.sis Publications
Many machine learning datasets are noisy with a substantial number of mislabeled instances. This noise yields sub-optimal classification performance. In this paper we study a large, low quality annotated dataset, created quickly and cheaply using Amazon Mechanical Turk to crowdsource annotations. We describe computationally cheap feature weighting techniques and a novel non-linear distribution spreading algorithm that can be used to iteratively and interactively correcting mislabeled instances to significantly improve annotation quality at low cost. Eight different emotion extraction experiments on Twitter data demonstrate that our approach is just as effective as more computationally expensive techniques. Our techniques save a considerable …
Hierarchical Interest Graph From Tweets, Pavan Kapanipathi, Prateek Jain, Chitra Venkataramani, Amit P. Sheth
Hierarchical Interest Graph From Tweets, Pavan Kapanipathi, Prateek Jain, Chitra Venkataramani, Amit P. Sheth
Kno.e.sis Publications
Industry and researchers have identified numerous ways to monetize microblogs for personalization and recommendation. A common challenge across these different works is the identification of user interests. Although techniques have been developed to address this challenge, a flexible approach that spans multiple levels of granularity in user interests has not been forthcoming. In this work, we focus on exploiting hierarchical semantics of concepts to infer richer user interests expressed as a Hierarchical Interest Graph. To create such graphs, we utilize users' tweets to first ground potential user interests to structured background knowledge such as Wikipedia Category Graph. We then adapt …
Cursing In English On Twitter, Wenbo Wang, Lu Chen, Krishnaprasad Thirunarayan, Amit P. Sheth
Cursing In English On Twitter, Wenbo Wang, Lu Chen, Krishnaprasad Thirunarayan, Amit P. Sheth
Kno.e.sis Publications
Cursing is not uncommon during conversations in the physical world: 0.5% to 0.7% of all the words we speak are curse words, given that 1% of all the words are first-person plural pronouns (e.g., we, us, our). On social media, people can instantly chat with friends without face-to-face interaction, usually in a more public fashion and broadly disseminated through highly connected social network. Will these distinctive features of social media lead to a change in people's cursing behavior? In this paper, we examine the characteristics of cursing activity on a popular social media platform - Twitter, involving the analysis of …
Location Prediction Of Twitter Users Using Wikipedia, Revathy Krishnamurthy, Pavan Kapanipathi, Amit P. Sheth, Krishnaprasad Thirunarayan
Location Prediction Of Twitter Users Using Wikipedia, Revathy Krishnamurthy, Pavan Kapanipathi, Amit P. Sheth, Krishnaprasad Thirunarayan
Kno.e.sis Publications
The mining of user generated content in social media has proven very effective in domains ranging from personalization and recommendation systems to crisis management. The knowledge of online users locations makes their tweets more informative and adds another dimension to their analysis. Existing approaches to predict the location of Twitter users are purely data-driven and require large training data sets of geo-tagged tweets. The collection and modelling process of tweets can be time intensive. To overcome this drawback, we propose a novel knowledge based approach that does not require any training data. Our approach uses information in Wikipedia, about cities …