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In this paper, we propose an approach to automatically detect sentiments on Twit-ter messages (tweets) that explores some characteristics of how tweets are written and meta-information of the words that compose these messages. Moreover, we leverage sources of noisy labels as our training data. These noisy labels were provided by a few sentiment detection(More)
Social media sites such as Twitter continue to grow at a fast pace. People of all generations use social media to exchange messages and share experiences of their life in a timely fashion. Most of these sites make their data available. An intriguing question is can we exploit this real-time and massive data-flow to improve business in a measurable way. In(More)
Social influence can be described as power-the ability of a person to influence the thoughts or actions of others. Identifying influential users on online social networks such as Twitter has been actively studied recently. In this paper, we investigate a modified k-shell decomposition algorithm for computing user influence on Twitter. The input to this(More)
Contextual question answering (QA), in which users' information needs are satisfied through an interactive QA dialogue, has recently attracted more research attention. One challenge of engaging dialogue into QA systems is to determine whether a question is relevant to the previous interaction context. We refer to this task as rel-evancy recognition. In this(More)
This paper proposes a learning approach for discovering the semantic structure of web pages. The task includes partitioning the text on a web page into information blocks and identifying their semantic categories. We employed two machine learning techniques, Adaboost and SVMs, to learn from a labeled web page corpus. We evaluated our approach on general web(More)
This tutorial highlights the characteristics of mobile search comparing with its desktop counterpart, reviews the state of art technologies of speech-based mobile search, and presents opportunities for exploiting multimodal interaction to optimize the efficiency of mobile search. It is suitable for students, researchers and practitioners working in the(More)