Institution
University of Illinois at Chicago
Education•Chicago, Illinois, United States•
About: University of Illinois at Chicago is a education organization based out in Chicago, Illinois, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 57071 authors who have published 110536 publications receiving 4264936 citations.
Topics: Population, Poison control, Health care, Cancer, Medicine
Papers published on a yearly basis
Papers
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11 Feb 2008TL;DR: This paper proposes a holistic lexicon-based approach to solving the problem of determining the semantic orientations (positive, negative or neutral) of opinions expressed on product features in reviews by exploiting external evidences and linguistic conventions of natural language expressions.
Abstract: One of the important types of information on the Web is the opinions expressed in the user generated content, e.g., customer reviews of products, forum posts, and blogs. In this paper, we focus on customer reviews of products. In particular, we study the problem of determining the semantic orientations (positive, negative or neutral) of opinions expressed on product features in reviews. This problem has many applications, e.g., opinion mining, summarization and search. Most existing techniques utilize a list of opinion (bearing) words (also called opinion lexicon) for the purpose. Opinion words are words that express desirable (e.g., great, amazing, etc.) or undesirable (e.g., bad, poor, etc) states. These approaches, however, all have some major shortcomings. In this paper, we propose a holistic lexicon-based approach to solving the problem by exploiting external evidences and linguistic conventions of natural language expressions. This approach allows the system to handle opinion words that are context dependent, which cause major difficulties for existing algorithms. It also deals with many special words, phrases and language constructs which have impacts on opinions based on their linguistic patterns. It also has an effective function for aggregating multiple conflicting opinion words in a sentence. A system, called Opinion Observer, based on the proposed technique has been implemented. Experimental results using a benchmark product review data set and some additional reviews show that the proposed technique is highly effective. It outperforms existing methods significantly
1,404 citations
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TL;DR: There is substantial evidence to support cognitive rehabilitation for people with TBI, including strategyTraining for mild memory impairment, strategy training for postacute attention deficits, and interventions for functional communication deficits.
1,390 citations
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TL;DR: Results indicate significant differences between entrepreneurs and managers on 4 personality dimensions such that entrepreneurs scored higher on Conscientiousness and Openness to Experience and lower on Neuroticism and Agreeableness.
Abstract: In this study, the authors used meta-analytical techniques to examine the relationship between personality and entrepreneurial status. Personality variables used in previous studies were categorized according to the five-factor model of personality. Results indicate significant differences between entrepreneurs and managers on 4 personality dimensions such that entrepreneurs scored higher on Conscientiousness and Openness to Experience and lower on Neuroticism and Agreeableness. No difference was found for Extraversion. Effect sizes for each personality dimension were small, although the multivariate relationship for the full set of personality variables was moderate (R = .37). Considerable heterogeneity existed for all of the personality variables except Agreeableness, suggesting that future research should explore possible moderators of the personality-entrepreneurial status relationship.
1,389 citations
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11 Feb 2008TL;DR: It is shown that opinion spam is quite different from Web spam and email spam, and thus requires different detection techniques, and therefore requires some novel techniques to detect them.
Abstract: Evaluative texts on the Web have become a valuable source of opinions on products, services, events, individuals, etc. Recently, many researchers have studied such opinion sources as product reviews, forum posts, and blogs. However, existing research has been focused on classification and summarization of opinions using natural language processing and data mining techniques. An important issue that has been neglected so far is opinion spam or trustworthiness of online opinions. In this paper, we study this issue in the context of product reviews, which are opinion rich and are widely used by consumers and product manufacturers. In the past two years, several startup companies also appeared which aggregate opinions from product reviews. It is thus high time to study spam in reviews. To the best of our knowledge, there is still no published study on this topic, although Web spam and email spam have been investigated extensively. We will see that opinion spam is quite different from Web spam and email spam, and thus requires different detection techniques. Based on the analysis of 5.8 million reviews and 2.14 million reviewers from amazon.com, we show that opinion spam in reviews is widespread. This paper analyzes such spam activities and presents some novel techniques to detect them
1,385 citations
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Nicholas J Kassebaum1, Amelia Bertozzi-Villa1, Megan Coggeshall1, Katya Anne Shackelford1 +349 more•Institutions (179)
TL;DR: Global rates of change suggest that only 16 countries will achieve the MDG 5 target by 2015, with evidence of continued acceleration in the MMR, and MMR was highest in the oldest age groups in both 1990 and 2013.
1,383 citations
Authors
Showing all 57433 results
Name | H-index | Papers | Citations |
---|---|---|---|
Meir J. Stampfer | 277 | 1414 | 283776 |
Frank B. Hu | 250 | 1675 | 253464 |
Lewis C. Cantley | 196 | 748 | 169037 |
Ronald Klein | 194 | 1305 | 149140 |
Anil K. Jain | 183 | 1016 | 192151 |
Yusuke Nakamura | 179 | 2076 | 160313 |
Bruce M. Spiegelman | 179 | 434 | 158009 |
Jie Zhang | 178 | 4857 | 221720 |
D. M. Strom | 176 | 3167 | 194314 |
Yury Gogotsi | 171 | 956 | 144520 |
Todd R. Golub | 164 | 422 | 201457 |
Rodney S. Ruoff | 164 | 666 | 194902 |
Philip A. Wolf | 163 | 459 | 114951 |
Barbara E.K. Klein | 160 | 856 | 93319 |
David Jonathan Hofman | 159 | 1407 | 140442 |