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Category: Text Mining

OpinoFetch: A Practical and Efficient Approach to Collecting Opinions on Arbitrary Entities

OpinoFetch: A Practical and Efficient Approach to Collecting Opinions on Arbitrary Entities

Abstract The abundance of opinions on the Web is now becoming a critical source of information in a variety of application areas such as business intelligence, market research and online shopping. Unfortunately, due to the rapid growth of online content, there is no one source to obtain a comprehensive set of opinions about a specific entity or a topic, making access to such content severely limited. While previous works have been focused on mining and summarizing online opinions, there is…

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Micropinion Generation: An Unsupervised Approach to Generating Ultra-Concise Summaries of Opinions

Micropinion Generation: An Unsupervised Approach to Generating Ultra-Concise Summaries of Opinions

This paper presents a new unsupervised approach to generating ultra-concise summaries of opinions. We formulate the problem of generating such a micropinion summary as an optimization problem, where we seek a set of concise and non-redundant phrases that are readable and represent key opinions in text. We measure representativeness based on a modified mutual information function and model readability with an n-gram language model.

Discovering Related Clinical Concepts Using Large Amounts of Clinical Notes

Discovering Related Clinical Concepts Using Large Amounts of Clinical Notes

Ganesan K, Lloyd S, Sarkar V. Discovering Related Clinical Concepts Using Large Amounts of Clinical Notes. Biomedical Engineering and Computational Biology. 2016;7(Suppl 2):27-33. doi:10.4137/BECB.S36155. Abstract The ability to find highly related clinical concepts is essential for many applications such as for hypothesis generation, query expansion for medical literature search, search results filtering, ICD-10 code filtering and many other applications. While manually constructed medical terminologies such as SNOMED CT can surface certain related concepts, these terminologies are inadequate as they depend on…

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Mining tag clouds and emoticons behind community feedback

Mining tag clouds and emoticons behind community feedback

Ganesan, K. A., N. Sundaresan, and H. Deo, “Mining tag clouds and emoticons behind community feedback“, WWW ’08: Proceeding of the 17th international conference on World Wide Web, Beijing, China, ACM, pp. 1181–1182, 2008. Abstract In this paper we describe our mining system which automatically mines tags from feedback text in an eCommerce scenario. It renders these tags in a visually appealing manner. Further, emoticons are attached to mined tags to add sentiment to the visual aspect. Download Paper http://dl.acm.org/authorize?943606 Related Articles…

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Multi-factor clustering for a marketplace search interface

Multi-factor clustering for a marketplace search interface

Abstract Search engines provide a small window to the vast repository of data they index and against which they search. They try their best to return the documents that are of relevance to the user but often a large number of results may be returned. Users struggle to manage this vast result set looking for the items of interest. Clustering search results is one way of alleviating this navigational pain. In this paper we describe a clustering system that enables…

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