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Tensor Decompositions and Applications

by Tamara G. Kolda, Brett W. Bader - SIAM REVIEW , 2009
"... This survey provides an overview of higher-order tensor decompositions, their applications, and available software. A tensor is a multidimensional or N -way array. Decompositions of higher-order tensors (i.e., N -way arrays with N ≥ 3) have applications in psychometrics, chemometrics, signal proce ..."
Abstract - Cited by 723 (18 self) - Add to MetaCart
processing, numerical linear algebra, computer vision, numerical analysis, data mining, neuroscience, graph analysis, etc. Two particular tensor decompositions can be considered to be higher-order extensions of the matrix singular value decompo- sition: CANDECOMP/PARAFAC (CP) decomposes a tensor as a sum

Support Vector Machine Classification and Validation of Cancer Tissue Samples Using Microarray Expression Data

by Terrence S. Furey, Nello Cristianini, Nigel Duffy, David W. Bednarski, Michèl Schummer, David Haussler , 2000
"... Motivation: DNA microarray experiments generating thousands of gene expression measurements, are being used to gather information from tissue and cell samples regarding gene expression differences that will be useful in diagnosing disease. We have developed a new method to analyse this kind of data ..."
Abstract - Cited by 569 (1 self) - Add to MetaCart
, and other normal tissues. The dataset consists of expression experiment results for 97 802 cDNAs for each tissue. As a result of computational analysis, a tissue sample is discovered and confirmed to be wrongly labeled. Upon correction of this mistake and the removal of an outlier, perfect classification

Extending Software Change Impact Analysis into COTS Components

by Shawn A. Bohner - 27TH ANNUAL NASA GODDARD/IEEE SOFTWARE ENGINEERING WORKSHOP (SEW-27’02) , 2003
"... As software components and middleware occupy more and more of the software engineering landscape, interoperability relationships point to increasingly relevant software change impacts. Packaged software now represents over thirty-two percent of the software in most organizations. While traceability ..."
Abstract - Cited by 246 (2 self) - Add to MetaCart
As software components and middleware occupy more and more of the software engineering landscape, interoperability relationships point to increasingly relevant software change impacts. Packaged software now represents over thirty-two percent of the software in most organizations. While traceability

Quantitative Analysis of Faults and Failures in a Complex Software System

by Norman E Fenton, Niclas Ohlsson - IEEE Transactions on Software Engineering , 2000
"... The dearth of published empirical data on major industrial systems has been one of the reasons that software engineering has failed to establish a proper scientific basis. In this paper we hope to provide a small contribution to the body of empirical knowledge. We describe a number of results from a ..."
Abstract - Cited by 208 (6 self) - Add to MetaCart
a quantitative study of faults and failures in two releases of a major commercial system. We tested a range of basic software engineering hypotheses relating to: the Pareto principle of distribution of faults and failures; the use of early fault data to predict later fault and failure data; metrics

Choosing Your Weapons: On Sentiment Analysis Tools for Software Engineering Research

by Robbert Jongeling, Subhajit Datta, Alexander Serebrenik
"... Abstract—Recent years have seen an increasing attention to social aspects of software engineering, including studies of emo-tions and sentiments experienced and expressed by the software developers. Most of these studies reuse existing sentiment analysis tools such as SentiStrength and NLTK. However ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
in an earlier study) as well as with each other. Fur-thermore, we evaluate the impact of the choice of a sentiment analysis tool on software engineering studies by conducting a simple study of differences in issue resolution times for positive, negative and neutral texts. We repeat the study for seven datasets

Bayesian Analysis of Empirical Software Engineering Cost Models

by Sunita Chulani, Barry Boehm, Bert Steece - IEEE Transactions on Software Engineering , 1999
"... To date many software engineering cost models have been developed to predict the cost, schedule and quality of the software under development. But, the rapidly changing nature of software development has made it extremely difficult to develop empirical models that continue to yield high prediction a ..."
Abstract - Cited by 109 (15 self) - Add to MetaCart
To date many software engineering cost models have been developed to predict the cost, schedule and quality of the software under development. But, the rapidly changing nature of software development has made it extremely difficult to develop empirical models that continue to yield high prediction

Exploiting Social Relations for Sentiment Analysis

by Xia Hu, Lei Tang, Jiliang Tang, Huan Liu - in Microblogging. Proc. WSDM , 2013
"... Microblogging, like Twitter 1, has become a popular platform of human expressions, through which users can easily produce content on breaking news, public events, or products. The massive amount of microblogging data is a useful and timely source that carries mass sentiment and opinions on various t ..."
Abstract - Cited by 46 (9 self) - Add to MetaCart
present a mathematical optimization formulation that incorporates the sentiment consistency and emotional contagion theories into the supervised learning process; and utilize sparse learning to tackle noisy texts in microblogging. An empirical study of two real-world Twitter datasets shows the superior

Sentiment analysis on Italian tweets

by Valerio Basile, Malvina Nissim
"... We describe TWITA, the first corpus of Italian tweets, which is created via a completely automatic procedure, portable to any other language. We experiment with sentiment analysis on two datasets from TWITA: a generic collection and a topic-specific collection. The only resource we use is a polarity ..."
Abstract - Cited by 10 (1 self) - Add to MetaCart
We describe TWITA, the first corpus of Italian tweets, which is created via a completely automatic procedure, portable to any other language. We experiment with sentiment analysis on two datasets from TWITA: a generic collection and a topic-specific collection. The only resource we use is a

Unsupervised sentiment analysis with emotional signals

by Xia Hu, Jiliang Tang, Huiji Gao, Huan Liu - In Proceedings of the 22nd international conference on World Wide Web, WWW’13. ACM
"... The explosion of social media services presents a great opportunity to understand the sentiment of the public via analyzing its large-scale and opinion-rich data. In social media, it is easy to amass vast quantities of unlabeled data, but very costly to obtain sentiment labels, which makes unsupervi ..."
Abstract - Cited by 27 (5 self) - Add to MetaCart
by providing a unified way to model two main categories of emotional signals, i.e., emotion indication and emotion correlation. We further incorporate the signals into an unsupervised learning framework for sentiment analysis. In the experiment, we compare the proposed framework with the state

Effective Sentiment Analysis of Social Media Datasets using Naive Bayesian Classification

by Dhiraj Gurkhe, Rishit Bhatia
"... Effective Sentiment Analysis Of Social Media Datasets Using Naive Bayesian Classification involves extraction of subjective in-formation from textual data. A normal human can easily understand the sentiment of a document written in natural language based on its knowledge of understanding the polarit ..."
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Effective Sentiment Analysis Of Social Media Datasets Using Naive Bayesian Classification involves extraction of subjective in-formation from textual data. A normal human can easily understand the sentiment of a document written in natural language based on its knowledge of understanding
Results 1 - 10 of 1,926