Computer Sciences Commons^™

Manipulation Of Online Reviews: An Analysis Of Ratings, Readability, And Sentiments, Nan Hu, Indranil Bose, Noi Sian Koh, Ling Liu

Research Collection School Of Computing and Information Systems

As consumers become increasingly reliant on online reviews to make purchase decisions, the sales of the product becomes dependent on the word of mouth (WOM) that it generates. As a result, there can be attempts by firms to manipulate online reviews of products to increase their sales. Despite the suspicion on the existence of such manipulation, the amount of such manipulation is unknown, and deciding which reviews to believe in is largely based on the reader's discretion and intuition. Therefore, the success of the manipulation of reviews by firms in generating sales of products is unknown. In this paper, we …

Discovering Fine-Grained Sentiment In Suicide Notes, Wenbo Wang, Lu Chen, Ming Tan, Shaojun Wang, Amit P. Sheth

Kno.e.sis Publications

This paper presents our solution for the i2b2 sentiment classification challenge. Our hybrid system consists of machine learning and rule-based classifiers. For the machine learning classifier, we investigate a variety of lexical, syntactic and knowledge-based features, and show how much these features contribute to the performance of the classifier through experiments. For the rule-based classifier, we propose an algorithm to automatically extract effective syntactic and lexical patterns from training examples. The experimental results show that the rule-based classifier outperforms the baseline machine learning classifier using unigram features. By combining the machine learning classifier and the rule-based classifier, the hybrid system …

A Semantic Problem Solving Environment For Integrative Parasite Research: Identification Of Intervention Targets For Trypanosoma Cruzi, Priti Parikh, Todd Minning, Vinh Nguyen, Sarasi Lalithsena, Amir H. Asiaee, Satya S. Sahoo, Prashant Doshi, Rick L. Tarleton, Amit P. Sheth

Kno.e.sis Publications

Background: Research on the biology of parasites requires a sophisticated and integrated computational platform to query and analyze large volumes of data, representing both unpublished (internal) and public (external) data sources. Effective analysis of an integrated data resource using knowledge discovery tools would significantly aid biologists in conducting their research, for example, through identifying various intervention targets in parasites, and in deciding the future direction of ongoing as well as planned projects. A key challenge in achieving this objective is the heterogeneity between the internal lab data, usually stored as flat files, Excel spreadsheets or custom-built databases, and the external …

Cognitive Approaches For The Semantic Web, Dedre Gentner, Frank Van Harmelen, Pascal Hitzler, Krzysztof Janowicz, Kai-Uwe Kuhnberger

Computer Science and Engineering Faculty Publications

A major focus in the design of Semantic Web ontology languages used to be on finding a suitable balance between the expressivity of the language and the tractability of reasoning services defined over this language. This focus mirrors the original vision of a Web composed of machine readable and understandable data. Similarly to the classical Web a few years ago, the attention is recently shifting towards a user-centric vision of the Semantic Web. Essentially, the information stored on the Web is from and for humans. This new focus is not only reflected in the fast growing Linked Data Web but …

Semantics Of Perception: Towards A Semantic Web Approach To Machine Perception, Cory Andrew Henson, Amit P. Sheth

Kno.e.sis Publications

The acts of observation and perception provide the building blocks for all human knowledge (Locke, 1690); they are the processes from which all ideas are born; and the sole bond connecting ourselves to the world around us. Now, with the advent of sensor networks capable of observation, this world may be directly accessible to machines. Missing from this vision, however, is the ability of machines to glean semantics from observation; to apprehend entities from detected qualities; to perceive. The systematic automation of this ability is the focus of machine perception -- the ability of computing machines to sense and interpret …

A Scalable Distributed Syntactic, Semantic And Lexical Language Model, Ming Tan, Wenli Zhou, Lei Zheng, Shaojun Wang

Kno.e.sis Publications

This paper presents an attempt at building a large scale distributed composite language model that is formed by seamlessly integrating an n-gram model, a structured language model, and probabilistic latent semantic analysis under a directed Markov random field paradigm to simultaneously account for local word lexical information, mid-range sentence syntactic structure, and long-span document semantic content. The composite language model has been trained by performing a convergent N-best list approximate EM algorithm and a follow-up EM algorithm to improve word prediction power on corpora with up to a billion tokens and stored on a supercomputer. The large scale distributed composite …

Alignment-Based Querying Of Linked Open Data, Amit Krishna Joshi, Prateek Jain, Pascal Hitzler, Peter Z. Yeh, Kunal Verma, Amit P. Sheth, Mariana Damova

Kno.e.sis Publications

The Linked Open Data (LOD) cloud is rapidly becoming the largest interconnected source of structured data on diverse domains. The potential of the LOD cloud is enormous, ranging from solving challenging AI issues such as open domain question answering to automated knowledge discovery. However, due to an inherent distributed nature of LOD and a growing number of ontologies and vocabularies used in LOD datasets, querying over multiple datasets and retrieving LOD data remains a challenging task. In this paper, we propose a novel approach to querying linked data by using alignments for processing queries whose constituent data come from heterogeneous …

Extracting Diverse Sentiment Expressions With Target-Dependent Polarity From Twitter, Lu Chen, Wenbo Wang, Meenakshi Nagarajan, Shaojun Wang, Amit P. Sheth

Kno.e.sis Publications

This study focuses on automatic extraction of sentiment expressions associated with given targets from Twitter. It addresses one of the key challenges in this work: Wide diversity and informal nature of sentiment expressions that cannot be trivially enumerated or captured using predefined lexical patterns.

On The Role Of Social Identity And Cohesion In Characterizing Online Social Communities, Hemant Purohit, Yiye Ruan, David Fuhry, Srinivasan Parthasarathy, Amit P. Sheth

Kno.e.sis Publications

Two prevailing theories for explaining social group or community structure are cohesion and identity. The social cohesion approach posits that social groups arise out of an aggregation of individuals that have mutual interpersonal attraction as they share common characteristics. These characteristics can range from common interests to kinship ties and from social values to ethnic backgrounds. In contrast, the social identity approach posits that an individual is likely to join a group based on an intrinsic self-evaluation at a cognitive or perceptual level. In other words group members typically share an awareness of a common category membership. In this work …

Role Of Semantic Web In Health Informatics, Satya S. Sahoo, Guo-Qiang Zhang, Amit P. Sheth

Kno.e.sis Publications

This tutorial weaves together three themes and the associated topics: [1] The role of biomedical ontologies [2] Key Semantic Web technologies with focus on Semantic provenance and integration [3] In-practice tools and real world use cases built to serve the needs of sleep medicine researchers, cardiologists involved in clinical practice, and work on vaccine development for human pathogens.

Semantics And Ontologies For Earthcube, Gary Berg-Cross, Isabel F. Cruz, Michael Dean, Timothy Finin, Mark Gahegan, Pascal Hitzler, Hook Hau, Krzysztof Janowicz, Naicong Li, Philip Murphy, Bryce Nordgren, Leo Obrst, Mark Schildhauer, Amit P. Sheth, Krishna Sinha, Anne Thessen, Nancy Wiegand, Ilya Zaslavasky

Kno.e.sis Publications

Semantic technologies and ontologies play an increasing role in scientific workflow systems and knowledge infrastructures. While ontologies are mostly used for the semantic annotation of metadata, semantic technologies enable searching metadata catalogs beyond simple keywords, with some early evidence of semantics used for data translation. However, the next generation of distributed and interdisciplinary knowledge infrastructures will require capabilities beyond simple subsumption reasoning over subclass relations. In this work, we report from the EarthCube Semantics Community by highlighting which role semantics and ontologies should play in the EarthCube knowledge infrastructure. We target the interested domain scientist and, thus, introduce the value …

Key Ingredients For Your Next Semantics Elevator Talk, Krzysztof Janowicz, Pascal Hitzler

Computer Science and Engineering Faculty Publications

2012 brought a major change to the semantics research community. Discussions on the use and benefits of semantic technologies are shifting away from the why to the how. Surprisingly this more in stakeholder interest is not accompanied by a more detailed understanding of what semantics research is about. Instead of blaming others for their (wrong) expectations, we need to learn how to emphasize the paradigm shift proposed by semantics research while abstracting from technical details and advocate the added value in a way that relates to the immediate needs of individual stakeholders without overselling. This paper highlights some of …

Open And Transparent: The Review Process Of The Semantic Web Journal, Krzysztof Janowicz, Pascal Hitzler

Computer Science and Engineering Faculty Publications

While open access is established in the world of academic publishing, open reviews are rare. The Semantic Web journal goes further than just open review by implementing an open and transparent review process in which reviews are publicly available, and the assigned editors and reviewers are known by name, and are published together with accepted manuscripts. In this article we introduce the steps to realize such a process from the conceptual design, over the implementation, a overview of the results so far, and up to lessons learned.

The Ontology For Parasite Lifecycle (Opl): Towards A Consistent Vocabulary Of Lifecycle Stages In Parasitic Organisms, Priti Parikh, Jie Zheng, Flora J. Logan-Klumpler, Christian J. Stoeckert, Pantelis Topalis, Anna Protasio, Amit P. Sheth, Mark Carrington, Matthew Berriman, Satya S. Sahoo

Kno.e.sis Publications

Background

Genome sequencing of many eukaryotic pathogens and the volume of data available on public resources have created a clear requirement for a consistent vocabulary to describe the range of developmental forms of parasites. Consistent labeling of experimental data and external data, in databases and the literature, is essential for integration, cross database comparison, and knowledge discovery. The primary objective of this work was to develop a dynamic and controlled vocabulary that can be used for various parasites. The paper describes the Ontology for Parasite Lifecycle (OPL) and discusses its application in parasite research.

Results

The OPL is based on …

Towards Cloud Mobile Hybrid Application Generation Using Semantically Enriched Domain Specific Languages, Ajith Harshana Ranabahu, Amit P. Sheth, Ashwin Manjunatha, Krishnaprasad Thirunarayan

Kno.e.sis Publications

The advancements in computing have resulted in a boom of cheap, ubiquitous, connected mobile devices as well as seemingly unlimited, utility style, pay as you go computing resources, commonly referred to as Cloud computing. Taking advantage of this computing landscape, however, has been hampered by the many heterogeneities that exist in the mobile space as well as the Cloud space.

This research attempts to introduce a disciplined methodology to develop Cloud-mobile hybrid applications by using a Domain Specific Language (DSL) centric approach to generate applications. A Cloud-mobile hybrid is an application that is split between a Cloud based back-end and …

Resident Identification Using Kinect Depth Image Data And Fuzzy Clustering Techniques, Tanvi Banerjee, James M. Keller, Marjorie Skubic

Kno.e.sis Publications

As a part of our passive fall risk assessment research in home environments, we present a method to identify older residents using features extracted from their gait information from a single depth camera. Depth images have been collected continuously for about eight months from several apartments at a senior housing facility. Shape descriptors such as bounding box information and image moments were extracted from silhouettes of the depth images. The features were then clustered using Possibilistic C Means for resident identification. This technology will allow researchers and health professionals to gather more information on the individual residents by filtering out …

Adding Within-Utterance Emotion Decay For More Human-Like Dialog, Michael Hans Durcholz

Open Access Theses & Dissertations

While spoken dialog systems have been used for commercial applications for several decades, most commercial spoken dialog systems provide only simple information exchange capabilities. Emotion synthesis in spoken dialog systems has become an active research area recently, and use of emotion-adaptive dialog systems has demonstrated improvements in user experience and rapport. This thesis seeks to improve how emotions are conveyed in dialog systems to enable robust emotional support that improves user experiences with dialog systems and models human speech characteristics more accurately than current dialog systems.

Prior work with Gracie (GRAduate Coordinator with Immediate response Emotions), an emotion-adaptive dialog system, …

Predictive Modeling For Navigating Social Media, Meiqun Hu

Dissertations and Theses Collection (Open Access)

Social media changes the way people use the Web. It has transformed ordinary Web users from information consumers to content contributors. One popular form of content contribution is social tagging, in which users assign tags to Web resources. By the collective efforts of the social tagging community, a new information space has been created for information navigation. Navigation allows serendipitous discovery of information by examining the information objects linked to one another in the social tagging space. In this dissertation, we study prediction tasks that facilitate navigation in social tagging systems. For social tagging systems to meet complex navigation needs …

Content Contribution In Social Media: The Case Of Youtube, Qian Tang, Bin Gu, Andrew B. Whinston

Research Collection School Of Computing and Information Systems

Social media allows individuals and businesses to contribute contents for public viewing. However, little is known about the underlying incentives that why content providers derive utilities from such activities. In this study, we build a dynamic structural model to recover the utility function for content providers. Our model distinguishes short-term payoffs based on ad revenue sharing from long-term payoffs driven by content providers' reputation. The model was estimated using a panel data of 914 top 1000 video providers on You Tube from Jun 7th, 2010, to Aug 7th, 2011 since top providers are more likely to be encouraged by these …

Tweets And Votes: A Study Of The 2011 Singapore General Election, Marko M. Skoric, Nathaniel D. Poor, Palakorn Achananuparp, Ee Peng Lim, Jing Jiang

Research Collection School Of Computing and Information Systems

This study focuses on the uses of Twitter during the elections, examining whether the messages posted online are reflective of the climate of public opinion. Using Twitter data obtained during the official campaign period of the 2011 Singapore General Election, we test the predictive power of tweets in forecasting the election results. In line with some previous studies, we find that during the elections the Twitter sphere represents a rich source of data for gauging public opinion and that the frequency of tweets mentioning names of political parties, political candidates and contested constituencies could be used to make predictions about …

Mining Diversity On Social Media Networks, Lu Liu, Feida Zhu, Meng Jiang, Jiawei Han, Lifeng Sun, Shiqiang Yang

Research Collection School Of Computing and Information Systems

The fast development of multimedia technology and increasing availability of network bandwidth has given rise to an abundance of network data as a result of all the ever-booming social media and social websites in recent years, e.g., Flickr, Youtube, MySpace, Facebook, etc. Social network analysis has therefore become a critical problem attracting enthusiasm from both academia and industry. However, an important measure that captures a participant’s diversity in the network has been largely neglected in previous studies. Namely, diversity characterizes how diverse a given node connects with its peers. In this paper, we give a comprehensive study of this concept. …

Overview Of Contrast Data Mining As A Field And Preview Of An Upcoming Book, Guozhu Dong, James Bailey

Kno.e.sis Publications

This report provides an overview of the field of contrast data mining and its applications, and offers a preview of an upcoming book on the topic. The importance of contrasting is discussed and a brief survey is given covering the following topics: general definitions and terminology for contrast patterns, representative contrast pattern mining algorithms, applications of contrast mining for fundamental data mining tasks such as classification and clustering, applications of contrast mining in bioinformatics, medicine, blog analysis, image analysis and subgroup mining, results on contrast based dataset similarity measure, and on analyzing item interaction in contrast patterns, and open research …

Computing Inconsistency Measure Based On Paraconsistent Semantics, Pascal Hitzler, Yue Ma, Guilin Qi

Computer Science and Engineering Faculty Publications

Measuring inconsistency in knowledge bases has been recognized as an important problem in several research areas. Many methods have been proposed to solve this problem and a main class of them is based on some kind of paraconsistent semantics. However, existing methods suffer from two limitations: (i) they are mostly restricted to propositional knowledge bases; (ii) very few of them discuss computational aspects of computing inconsistency measures. In this article, we try to solve these two limitations by exploring algorithms for computing an inconsistency measure of first-order knowledge bases. After introducing a four-valued semantics for first-order logic, we define an …

Value Relevance Of Blog Visibility, Nan Hu, Ling Liu, Arindam Tripathy, Lee J. Yao

Research Collection School Of Computing and Information Systems

This study empirically examines the effect of a non-traditional information source, namely a firm's blog visibility on the capital market valuation of firms. After controlling for earnings, book value of equity and other value relevant variables, such as traditional media exposure, R&D spending, and advertising expense, we find a positive association between a firm's blog visibility and its capital market valuation. In addition, we find blog visibility Grange causes trading, not vice versa. Our findings indicate that non-traditional information sources such as blogs help disseminate information and influence consumers' investment decisions by capturing their attention.

Modeling Social Strength In Social Media Community Via Kernel-Based Learning, Jinfeng Zhuang, Tao Mei, Steven C. H. Hoi, Xian-Sheng Hua, Shipeng Li

Research Collection School Of Computing and Information Systems

Modeling continuous social strength rather than conventional binary social ties in the social network can lead to a more precise and informative description of social relationship among people. In this paper, we study the problem of social strength modeling (SSM) for the users in a social media community, who are typically associated with diverse form of data. In particular, we take Flickr---the most popular online photo sharing community---as an example, in which users are sharing their experiences through substantial amounts of multimodal contents (e.g., photos, tags, geo-locations, friend lists) and social behaviors (e.g., commenting and joining interest groups). Such heterogeneous …

Context-Based Friend Suggestion In Online Photo-Sharing Community, Ting Yao, Chong-Wah Ngo, Tao Mei

Research Collection School Of Computing and Information Systems

With the popularity of social media, web users tend to spend more time than before for sharing their experience and interest in online photo-sharing sites. The wide variety of sharing behaviors generate different metadata which pose new opportunities for the discovery of communities. We propose a new approach, named context-based friend suggestion, to leverage the diverse form of contextual cues for more effective friend suggestion in the social media community. Different from existing approaches, we consider both visual and geographical cues, and develop two user-based similarity measurements, i.e., visual similarity and geo similarity for characterizing user relationship. The problem of …

Content Contribution Under Revenue Sharing And Reputation Concern In Social Media: The Case Of Youtube, Qian Tang, Bin Gu, Andrew B. Whinston

Research Collection School Of Computing and Information Systems

A key feature of social media is that it allows individuals and businesses to contribute contents for public viewing. However, little is known about how content providers derive payoffs from such activities. In this study, we build a dynamic structural model to recover the utility function for content providers. Our model distinguishes short-term payoffs based on ad revenue sharing from long-term payoffs driven by content providers’ reputation. The model was estimated using a panel data of 914 top 1000 providers and 381 randomly selected providers on YouTube from Jun 7th, 2010, to Aug 7th, 2011. The two different sets of …

Wsm 2011: Third Acm Workshop On Social Media, Steven C. H. Hoi, Michal Jacovi, Ioannis Kompatsiaris, Jiebo Luo, Konstantinos Tserpes

Research Collection School Of Computing and Information Systems

The Third Workshop on Social Media (WSM2011) continues the series of Workshops on Social Media in 2009 and 2010 and has been established as a platform for the presentation and discussion of the latest, key research issues in social media analysis, exploration, search, mining, and emerging new social media applications. It is held in conjunction with the ACM International Multimedia Conference (MM'11) at Scottsdale, Arizona, USA, 2011 and has attracted contributions on various aspects of social media including data mining from social media, content organization, geo-localization, personalization, recommendation systems, user experience, machine learning and social media approaches and architectures for …

Coping With Distance: An Empirical Study Of Communication On The Jazz Platform, Renuka Sindhgatta, Bikram Sengupta, Subhajit Datta

Research Collection School Of Computing and Information Systems

Global software development - which is characterized by teams separated by physical distance and/or time-zone differences - has traditionally posed significant communication challenges. Often these have caused delays in completing tasks, or created misalignment across sites leading to re-work. In recent years, however, a new breed of development environments with rich collaboration features have emerged to facilitate cross-site work in distributed projects. In this paper we revisit the question "does distance matter?" in the context of IBM Jazz Platform -- a state-of-the-art collaborative development environment. We study the ecosystem of a large distributed team of around 300 members across 35 …

Exploring Tweets Normalization And Query Time Sensitivity For Twitter Search, Zhongyu Wei, Wei Gao, Lanjun Zhou, Binyang Li, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

This paper presents our work for the Realtime Adhoc Task of TREC 2011 Microblog Track. Microblog texts like tweets are generally characterized by the inclusion of a large proportion of irregular expressions, such as ill-formed words, which can lead to significant mismatch between query terms and tweets. In addition, Twitter queries are distinguished from Web queries with many unique characteristics, one of which reflects the clearly distinct temporal aspects of Twitter search behavior. In this study, we deal with the first problem by normalizing tweet texts and the second by capturing the temporal characteristics of topic. We divided topics into …