Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Institution
-
- Singapore Management University (2961)
- Wright State University (632)
- Walden University (447)
- Selected Works (287)
- New Jersey Institute of Technology (137)
-
- University of Nebraska at Omaha (119)
- California State University, San Bernardino (96)
- Old Dominion University (95)
- San Jose State University (85)
- University of Dayton (82)
- The University of Maine (67)
- City University of New York (CUNY) (65)
- University of Nebraska - Lincoln (54)
- Air Force Institute of Technology (53)
- SelectedWorks (53)
- Technological University Dublin (51)
- University of South Florida (50)
- Kennesaw State University (46)
- Nova Southeastern University (43)
- Claremont Colleges (42)
- University of Wisconsin Milwaukee (42)
- University of Arkansas, Fayetteville (41)
- Western Kentucky University (41)
- Dakota State University (39)
- Institute of Business Administration (38)
- California Polytechnic State University, San Luis Obispo (36)
- Western University (35)
- Ateneo de Manila University (34)
- Governors State University (34)
- Purdue University (34)
- Keyword
-
- Machine learning (101)
- Information technology (93)
- Data mining (89)
- Social media (78)
- Twitter (64)
-
- Machine Learning (57)
- Cybersecurity (54)
- Semantic Web (54)
- Deep learning (52)
- Artificial intelligence (49)
- Online learning (49)
- Information Technology (47)
- Classification (46)
- Cloud computing (45)
- Information retrieval (45)
- Privacy (45)
- Big data (44)
- Database (43)
- Ontology (43)
- Computer science (42)
- Information security (41)
- Algorithms (40)
- Security (40)
- Databases (39)
- Information systems (39)
- Management (37)
- Clustering (36)
- Data Mining (36)
- Northern Ohio Data and Information Service (NODIS) (36)
- Technology (35)
- Publication Year
- Publication
-
- Research Collection School Of Computing and Information Systems (2867)
- Kno.e.sis Publications (541)
- Walden Dissertations and Doctoral Studies (447)
- Theses and Dissertations (116)
- Dissertations (107)
-
- Computer Science Faculty Publications (91)
- Computer Science and Engineering Faculty Publications (91)
- Theses Digitization Project (84)
- Master's Projects (68)
- Information Systems and Quantitative Analysis Faculty Proceedings & Presentations (64)
- Electronic Theses and Dissertations (55)
- Dissertations and Theses Collection (Open Access) (50)
- Theses (46)
- USF Tampa Graduate Theses and Dissertations (46)
- CCE Theses and Dissertations (42)
- Information Systems and Quantitative Analysis Faculty Publications (41)
- Kyriakos MOURATIDIS (40)
- CGU Faculty Publications and Research (37)
- International Conference on Information and Communication Technologies (36)
- Open Educational Resources (34)
- Department of Information Systems & Computer Science Faculty Publications (33)
- All Capstone Projects (32)
- Graduate Theses and Dissertations (32)
- Masters Theses & Doctoral Dissertations (32)
- Articles (29)
- Conference papers (28)
- David LO (28)
- Journal of Spatial Information Science (28)
- All Maxine Goodman Levin School of Urban Affairs Publications (27)
- Saverio Perugini (25)
- Publication Type
Articles 2221 - 2250 of 6720
Full-Text Articles in Physical Sciences and Mathematics
Esg And Corporate Financial Performance: Empirical Evidence From China's Listed Power Generation Companies, Changhong Zhao, Yu Guo, Jiahai Yuan, Mengya Wu, Daiyu Li, Yiou Zhou, Jiangang Kang
Esg And Corporate Financial Performance: Empirical Evidence From China's Listed Power Generation Companies, Changhong Zhao, Yu Guo, Jiahai Yuan, Mengya Wu, Daiyu Li, Yiou Zhou, Jiangang Kang
Research Collection School Of Computing and Information Systems
Nowadays, listed companies around the world are shifting from short-term goals of maximizing profits to long-term sustainable environmental, social, and governance (ESG) goals. People have come to realize that ESG has become an important source of the corporate risk and may affect the company's financial performance and profitability. Recent research shows that good ESG performance could improve the financial performance in some countries. Yet, the question of how does ESG affect financial performance has not been thoroughly discussed and studied in China. In this article, we study China's listed power generation groups to explore the relationship between ESG performance and …
Neural Collective Entity Linking, Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu
Neural Collective Entity Linking, Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu
Research Collection School Of Computing and Information Systems
Entity Linking aims to link entity mentions in texts to knowledge bases, and neural models have achieved recent success in this task. However, most existing methods rely on local contexts to resolve entities independently, which may usually fail due to the data sparsity of local information. To address this issue, we propose a novel neural model for collective entity linking, named as NCEL. NCEL applies Graph Convolutional Network to integrate both local contextual features and global coherence information for entity linking. To improve the computation efficiency, we approximately perform graph convolution on a subgraph of adjacent entity mentions instead of …
Offline Versus Online: A Meaningful Categorization Of Ties For Retweets, Felicia Natali, Feida Zhu
Offline Versus Online: A Meaningful Categorization Of Ties For Retweets, Felicia Natali, Feida Zhu
Research Collection School Of Computing and Information Systems
With the recent proliferation of news being shared through online social networks, it is crucial to determine how news is spread and what drives people to share certain stories. In this paper, we focus on the social networking site Twitter and analyse user’s retweets. We study retweeting patterns between offline and online friends, particularly, how tweet novelty and tweet topic differ between tweets retweeted by offline friends and those retweeted by online friends.
Embedding Wordnet Knowledge For Textual Entailment, Yunshi Lan, Jing Jiang
Embedding Wordnet Knowledge For Textual Entailment, Yunshi Lan, Jing Jiang
Research Collection School Of Computing and Information Systems
In this paper, we study how we can improve a deep learning approach to textual entailment by incorporating lexical entailment relations from WordNet. Our idea is to embed the lexical entailment knowledge contained in WordNet in specially-learned word vectors, which we call “entailment vectors.” We present a standard neural network model and a novel set-theoretic model to learn these entailment vectors from word pairs with known lexical entailment relations derived from WordNet. We further incorporate these entailment vectors into a decomposable attention model for textual entailment and evaluate the model on the SICK and the SNLI dataset. We find that …
Unusual Events In Github Repositories, Christoph Treude, Larissa Leite, Maurício Aniche
Unusual Events In Github Repositories, Christoph Treude, Larissa Leite, Maurício Aniche
Research Collection School Of Computing and Information Systems
In large and active software projects, it becomes impractical for a developer to stay aware of all project activity. While it might not be necessary to know about each commit or issue, it is arguably important to know about the ones that are unusual. To investigate this hypothesis, we identified unusual events in 200 GitHub projects using a comprehensive list of ways in which an artifact can be unusual and asked 140 developers responsible for or affected by these events to comment on the usefulness of the corresponding information. Based on 2,096 answers, we identify the subset of unusual events …
Evaluation Criteria For Selecting Nosql Databases In A Single Box Environment, Ryan D. Engle, Brent T. Langhals, Michael R. Grimaila, Douglas D. Hodson
Evaluation Criteria For Selecting Nosql Databases In A Single Box Environment, Ryan D. Engle, Brent T. Langhals, Michael R. Grimaila, Douglas D. Hodson
Faculty Publications
In recent years, NoSQL database systems have become increasingly popular, especially for big data, commercial applications. These systems were designed to overcome the scaling and flexibility limitations plaguing traditional relational database management systems (RDBMSs). Given NoSQL database systems have been typically implemented in large-scale distributed environments serving large numbers of simultaneous users across potentially thousands of geographically separated devices, little consideration has been given to evaluating their value within single-box environments. It is postulated some of the inherent traits of each NoSQL database type may be useful, perhaps even preferable, regardless of scale. Thus, this paper proposes criteria conceived to …
A Characterization Of The Medical-Legal Partnership (Mlp) Of Nebraska Medicine, Jordan Pieper
A Characterization Of The Medical-Legal Partnership (Mlp) Of Nebraska Medicine, Jordan Pieper
Capstone Experience
This research study was completed at Legal Aid of Nebraska’s Health, Education, and Law Project through the partnership it has formed working with Nebraska Medicine and Iowa Legal Aid. Traditionally, health and disease have always been viewed exclusively as "healthcare" issues. But with healthcare consistently growing towards holistic approaches to help patients, we now know there are deeper, structural conditions of society that can act as strong driving forces of a person's poor daily living conditions that can negatively impact health. The importance of a Medical-Legal Partnership is that it considers a patient's social determinants of health (SDHs). The goal …
Knowledge As A Bridge: Improving Cross-Domain Answer Selection With External Knowledge, Yang Deng, Ying Shen, Min Yang, Yaliang Li, Nan Du, Wei Fan, Kai Lei
Knowledge As A Bridge: Improving Cross-Domain Answer Selection With External Knowledge, Yang Deng, Ying Shen, Min Yang, Yaliang Li, Nan Du, Wei Fan, Kai Lei
Research Collection School Of Computing and Information Systems
Answer selection is an important but challenging task. Significant progresses have been made in domains where a large amount of labeled training data is available. However, obtaining rich annotated data is a time-consuming and expensive process, creating a substantial barrier for applying answer selection models to a new domain which has limited labeled data. In this paper, we propose Knowledge-aware Attentive Network (KAN), a transfer learning framework for cross-domain answer selection, which uses the knowledge base as a bridge to enable knowledge transfer from the source domain to the target domains. Specifically, we design a knowledge module to integrate the …
Exact Processing Of Uncertain Top-K Queries In Multi-Criteria Settings, Kyriakos Mouratidis, Bo Tang
Exact Processing Of Uncertain Top-K Queries In Multi-Criteria Settings, Kyriakos Mouratidis, Bo Tang
Research Collection School Of Computing and Information Systems
Traditional rank-aware processing assumes a dataset that contains available options to cover a specific need (e.g., restaurants, hotels, etc) and users who browse that dataset via top-k queries with linear scoring functions, i.e., by ranking the options according to the weighted sum of their attributes, for a set of given weights. In practice, however, user preferences (weights) may only be estimated with bounded accuracy, or may be inherently uncertain due to the inability of a human user to specify exact weight values with absolute accuracy. Motivated by this, we introduce the uncertain top-k query (UTK). Given uncertain preferences, that is, …
Transaction Cost Optimization For Online Portfolio Selection, Bin Li, Jialei Wang, Dingjiang Huang, Steven C. H. Hoi
Transaction Cost Optimization For Online Portfolio Selection, Bin Li, Jialei Wang, Dingjiang Huang, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
To improve existing online portfolio selection strategies in the case of non-zero transaction costs, we propose a novel framework named Transaction Cost Optimization (TCO). The TCO framework incorporates the L1 norm of the difference between two consecutive allocations together with the principles of maximizing expected log return. We further solve the formulation via convex optimization, and obtain two closed-form portfolio update formulas, which follow the same principle as Proportional Portfolio Rebalancing (PPR) in industry. We empirically evaluate the proposed framework using four commonly used data-sets. Although these data-sets do not consider delisted firms and are thus subject to survival bias, …
Trajectory-Driven Influential Billboard Placement, Ping Zhang, Zhifeng Bao, Yuchen Li, Guoliang Li, Yipeng Zhang, Zhiyong Peng
Trajectory-Driven Influential Billboard Placement, Ping Zhang, Zhifeng Bao, Yuchen Li, Guoliang Li, Yipeng Zhang, Zhiyong Peng
Research Collection School Of Computing and Information Systems
In this paper we propose and study the problem of trajectory-driven influential billboard placement: given a set of billboards U (each with a location and a cost), a database of trajectories T and a budget L, find a set of billboards within the budget to influence the largest number of trajectories. One core challenge is to identify and reduce the overlap of the influence from different billboards to the same trajectories, while keeping the budget constraint into consideration. We show that this problem is NP-hard and present an enumeration based algorithm with (1−1/e) approximation ratio. However, the enumeration should be …
Fostering The Retrieval Of Suitable Web Resources In Response To Children's Educational Search Tasks, Oghenemaro Deborah Anuyah
Fostering The Retrieval Of Suitable Web Resources In Response To Children's Educational Search Tasks, Oghenemaro Deborah Anuyah
Boise State University Theses and Dissertations
Children regularly turn to search engines (SEs) to locate school-related materials. Unfortunately, research has shown that when utilizing SEs, children do not always access resources that specifically target them. To support children, popular and child-oriented SEs make available a safe search filter, which is meant to eliminate inappropriate resources. Safe search is, however, not always the perfect deterrent as pornographic and hate-based resources may slip through the filter, while resources relevant to an educational search context may be misconstrued and filtered out. Moreover, filtering inappropriate resources in response to children searches is just one perspective to consider in offering them …
Learning Representations Of Ultrahigh-Dimensional Data For Random Distance-Based Outlier Detection, Guansong Pang, Longbing Cao, Ling Chen, Defu Lian, Huan Liu
Learning Representations Of Ultrahigh-Dimensional Data For Random Distance-Based Outlier Detection, Guansong Pang, Longbing Cao, Ling Chen, Defu Lian, Huan Liu
Research Collection School Of Computing and Information Systems
Learning expressive low-dimensional representations of ultrahigh-dimensional data, e.g., data with thousands/millions of features, has been a major way to enable learning methods to address the curse of dimensionality. However, existing unsupervised representation learning methods mainly focus on preserving the data regularity information and learning the representations independently of subsequent outlier detection methods, which can result in suboptimal and unstable performance of detecting irregularities (i.e., outliers).This paper introduces a ranking model-based framework, called RAMODO, to address this issue. RAMODO unifies representation learning and outlier detection to learn low-dimensional representations that are tailored for a state-of-the-art outlier detection approach - the random …
Probabilistic Collaborative Representation Learning For Personalized Item Recommendation, Aghiles Salah, Hady W. Lauw
Probabilistic Collaborative Representation Learning For Personalized Item Recommendation, Aghiles Salah, Hady W. Lauw
Research Collection School Of Computing and Information Systems
We present Probabilistic Collaborative Representation Learning (PCRL), a new generative model of user preferences and item contexts. The latter builds on the assumption that relationships among items within contexts (e.g., browsing session, shopping cart, etc.) may underlie various aspects that guide the choices people make. Intuitively, PCRL seeks representations of items reflecting various regularities between them that might be useful at explaining user preferences. Formally, it relies on Bayesian Poisson Factorization to model user-item interactions, and uses a multilayered latent variable architecture to learn representations of items from their contexts. PCRL seamlessly integrates both tasks within a joint framework. However, …
Customer Segmentation Using Online Platforms: Isolating Behavioral And Demographic Segments For Persona Creation Via Aggregated User Data, Jisun An, Haewoon Kwak, Soon‑Gyo Jung, Joni Salminen, Bernard J. Jansen
Customer Segmentation Using Online Platforms: Isolating Behavioral And Demographic Segments For Persona Creation Via Aggregated User Data, Jisun An, Haewoon Kwak, Soon‑Gyo Jung, Joni Salminen, Bernard J. Jansen
Research Collection School Of Computing and Information Systems
We propose a novel approach for isolating customer segments using online customer data for products that are distributed via online social media platforms. We use non-negative matrix factorization to first identify behavioral customer segments and then to identify demographic customer segments. We employ a methodology for linking the two segments to present integrated and holistic customer segments, also known as personas. Behavioral segments are generated from customer interactions with online content. Demographic segments are generated using the gender, age, and location of these customers. In addition to evaluating our approach, we demonstrate its practicality via a system leveraging these customer …
Cryptovisor: A Cryptocurrency Advisor Tool, Matthew Baldree, Paul Widhalm, Brandon Hill, Matteo Ortisi
Cryptovisor: A Cryptocurrency Advisor Tool, Matthew Baldree, Paul Widhalm, Brandon Hill, Matteo Ortisi
SMU Data Science Review
In this paper, we present a tool that provides trading recommendations for cryptocurrency using a stochastic gradient boost classifier trained from a model labeled by technical indicators. The cryptocurrency market is volatile due to its infancy and limited size making it difficult for investors to know when to enter, exit, or stay in the market. Therefore, a tool is needed to provide investment recommendations for investors. We developed such a tool to support one cryptocurrency, Bitcoin, based on its historical price and volume data to recommend a trading decision for today or past days. This tool is 95.50% accurate with …
Machine Learning To Predict College Course Success, Anthony R.Y. Dalton, Justin Beer, Sriharshasai Kommanapalli, James S. Lanich Ph.D.
Machine Learning To Predict College Course Success, Anthony R.Y. Dalton, Justin Beer, Sriharshasai Kommanapalli, James S. Lanich Ph.D.
SMU Data Science Review
In this paper, we present an analysis of the predictive ability of machine learning on the success of students in college courses in a California Community College. The California Legislature passed assembly bill 705 in order to place students in non-remedial coursework, based on high school transcripts, to increase college completion. We utilize machine learning methods on de-identified student high school transcript data to create predictive algorithms on whether or not the student will be successful in college-level English and Mathematics coursework. To satisfy the bill’s requirements, we first use exploratory data analysis on applicable transcript variables. Then we use …
Analytics For Novel Consumer Insights (A Three Essay Dissertation), Utkarsh Shrivastava
Analytics For Novel Consumer Insights (A Three Essay Dissertation), Utkarsh Shrivastava
USF Tampa Graduate Theses and Dissertations
Both literature and practice have investigated how the vast amount of ever increasing customer information can inform marketing strategy and decision making. However, the customer data is often susceptible to modeling bias and misleading findings due to various factors including sample selection and unobservable variables. The available analytics toolkit has continued to develop but in the age of nearly perfect information, the customer decision making has also evolved. The dissertation addresses some of the challenges in deriving valid and useful consumer insights from customer data in the digital age. The first study addresses the limitations of traditional customer purchase measures …
Predicting River Stage Using Recurrent Neural Networks, Eric Rohli
Predicting River Stage Using Recurrent Neural Networks, Eric Rohli
LSU Master's Theses
River stage prediction is an important problem in the water transportation industry. Accurate river stage predictions provide crucial information to barge and tow boat operators, port terminal captains, and lock management officials. Shallow river levels caused by prolonged drought impact the loading capacity of barges and tow boats. High river levels caused by excessive rainfall or snowmelt allow for greater tow capacities but make downstream transportation and lock management risky. Current academic river height prediction systems utilize either time series statistical analysis or machine learning algorithms to forecast future river heights, but systems that combine these two areas often limit …
Mining Temporal Activity Patterns On Social Media, Nikan Chavoshi
Mining Temporal Activity Patterns On Social Media, Nikan Chavoshi
Computer Science ETDs
Social media provide communication networks for their users to easily create and share content. Automated accounts, called bots, abuse these platforms by engaging in suspicious and/or illegal activities. Bots push spam content and participate in sponsored activities to expand their audience. The prevalence of bot accounts in social media can harm the usability of these platforms, and decrease the level of trustworthiness in them. The main goal of this dissertation is to show that temporal analysis facilitates detecting bots in social media. I introduce new bot detection techniques which exploit temporal information. Since automated accounts are controlled by computer programs, …
Creating Real-Time Dynamic Knowledge Graphs, Swati Padhee, Sarasi Lalithsena, Amit P. Sheth
Creating Real-Time Dynamic Knowledge Graphs, Swati Padhee, Sarasi Lalithsena, Amit P. Sheth
Kno.e.sis Publications
No abstract provided.
Kbase: The United States Department Of Energy Systems Biology Knowledgebase, Adam P. Arkin, Robert W. Cottingham, Christopher S. Henry, Nomi L. Harris, Rick L. Stevens, Sergei Maslov, Doreen Ware, Fernando Perez, Shane Canon, Michael W. Sneddon, Matthew L. Henderson, William J. Riehl, Dan Murphy-Olson, Stephen Y. Chan, Roy T. Kamimura, Sunita Kumari, Meghan M. Drake, Thomas S. Brettin, Elizabeth M. Glass, Dylan Chivian, Dan Gunter, David J. Weston, Benjamin H. Allen, Jason Baumohl, Nathan L. Tintle
Kbase: The United States Department Of Energy Systems Biology Knowledgebase, Adam P. Arkin, Robert W. Cottingham, Christopher S. Henry, Nomi L. Harris, Rick L. Stevens, Sergei Maslov, Doreen Ware, Fernando Perez, Shane Canon, Michael W. Sneddon, Matthew L. Henderson, William J. Riehl, Dan Murphy-Olson, Stephen Y. Chan, Roy T. Kamimura, Sunita Kumari, Meghan M. Drake, Thomas S. Brettin, Elizabeth M. Glass, Dylan Chivian, Dan Gunter, David J. Weston, Benjamin H. Allen, Jason Baumohl, Nathan L. Tintle
Faculty Work Comprehensive List
No abstract provided.
Adopt: Combining Parameter Tuning And Adaptive Operator Ordering For Solving A Class Of Orienteering Problems, Aldy Gunawan, Hoong Chuin Lau, Kun Lu
Adopt: Combining Parameter Tuning And Adaptive Operator Ordering For Solving A Class Of Orienteering Problems, Aldy Gunawan, Hoong Chuin Lau, Kun Lu
Research Collection School Of Computing and Information Systems
Two fundamental challenges in local search based metaheuristics are how to determine parameter configurations and design the underlying Local Search (LS) procedure. In this paper, we propose a framework in order to handle both challenges, called ADaptive OPeraTor Ordering (ADOPT). In this paper, The ADOPT framework is applied to two metaheuristics, namely Iterated Local Search (ILS) and a hybridization of Simulated Annealing and ILS (SAILS) for solving two variants of the Orienteering Problem: the Team Dependent Orienteering Problem (TDOP) and the Team Orienteering Problem with Time Windows (TOPTW). This framework consists of two main processes. The Design of Experiment (DOE) …
A Bayesian Latent Variable Model Of User Preferences With Item Context, Aghiles Salah, Hady W. Lauw
A Bayesian Latent Variable Model Of User Preferences With Item Context, Aghiles Salah, Hady W. Lauw
Research Collection School Of Computing and Information Systems
Personalized recommendation has proven to be very promising in modeling the preference of users over items. However, most existing work in this context focuses primarily on modeling user-item interactions, which tend to be very sparse. We propose to further leverage the item-item relationships that may reflect various aspects of items that guide users’ choices. Intuitively, items that occur within the same “context” (e.g., browsed in the same session, purchased in the same basket) are likely related in some latent aspect. Therefore, accounting for the item’s context would complement the sparse user-item interactions by extending a user’s preference to other items …
Probabilistic Guided Exploration For Reinforcement Learning In Self-Organizing Neural Networks, Peng Wang, Weigui Jair Zhou, Di Wang, Ah-Hwee Tan
Probabilistic Guided Exploration For Reinforcement Learning In Self-Organizing Neural Networks, Peng Wang, Weigui Jair Zhou, Di Wang, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Exploration is essential in reinforcement learning, which expands the search space of potential solutions to a given problem for performance evaluations. Specifically, carefully designed exploration strategy may help the agent learn faster by taking the advantage of what it has learned previously. However, many reinforcement learning mechanisms still adopt simple exploration strategies, which select actions in a pure random manner among all the feasible actions. In this paper, we propose novel mechanisms to improve the existing knowledgebased exploration strategy based on a probabilistic guided approach to select actions. We conduct extensive experiments in a Minefield navigation simulator and the results …
Pacela: A Neural Framework For User Visitation In Location-Based Social Networks, Thanh Nam Doan, Ee-Peng Lim
Pacela: A Neural Framework For User Visitation In Location-Based Social Networks, Thanh Nam Doan, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Check-in prediction using location-based social network data is an important research problem for both academia and industry since an accurate check-in predictive model is useful to many applications, e.g. urban planning, venue recommendation, route suggestion, and context-aware advertising. Intuitively, when considering venues to visit, users may rely on their past observed visit histories as well as some latent attributes associated with the venues. In this paper, we therefore propose a check-in prediction model based on a neural framework called Preference and Context Embeddings with Latent Attributes (PACELA). PACELA learns the embeddings space for the user and venue data as well …
Context Recovery In Location-Based Social Networks, Wen Haw Chong
Context Recovery In Location-Based Social Networks, Wen Haw Chong
Dissertations and Theses Collection (Open Access)
This dissertation addresses context recovery in Location-Based Social Networks (LBSN), which are platforms where users post content from various locations. With this general LBSN definition, many existing social media platforms that support user-generated location relevant content using mobile devices could also qualify as LBSNs. Context recovery for such user posts refers to recovering the venue and the semantic contexts of these user posts. Such information is useful for user profiling and to support various applications such as venue recommendation and location- based advertising.
Detecting Personal Intake Of Medicine From Twitter, Debanjan Mahata, Jasper Friedrichs, Rajiv Ratn Shah, Jing Jiang
Detecting Personal Intake Of Medicine From Twitter, Debanjan Mahata, Jasper Friedrichs, Rajiv Ratn Shah, Jing Jiang
Research Collection School Of Computing and Information Systems
Mining social media messages such as tweets, blogs, and Facebook posts for health and drug related information has received significant interest in pharmacovigilance research. Social media sites (e.g., Twitter), have been used for monitoring drug abuse, adverse reactions to drug usage, and analyzing expression of sentiments related to drugs. Most of these studies are based on aggregated results from a large population rather than specific sets of individuals. In order to conduct studies at an individual level or specific groups of people, identifying posts mentioning intake of medicine by the user is necessary. Toward this objective we develop a classifier …
Searching For The X-Factor: Exploring Corpus Subjectivity For Word Embeddings, Maksim Tkachenko, Chong Cher Chia, Hady W. Lauw
Searching For The X-Factor: Exploring Corpus Subjectivity For Word Embeddings, Maksim Tkachenko, Chong Cher Chia, Hady W. Lauw
Research Collection School Of Computing and Information Systems
We explore the notion of subjectivity, and hypothesize that word embeddings learnt from input corpora of varying levels of subjectivity behave differently on natural language processing tasks such as classifying a sentence by sentiment, subjectivity, or topic. Through systematic comparative analyses, we establish this to be the case indeed. Moreover, based on the discovery of the outsized role that sentiment words play on subjectivity-sensitive tasks such as sentiment classification, we develop a novel word embedding SentiVec which is infused with sentiment information from a lexical resource, and is shown to outperform baselines on such tasks.
Disease Gene Classification With Metagraph Representations, Sezin Kircali Ata, Yuan Fang, Min Wu, Xiao-Li Li, Xiaokui Xiao
Disease Gene Classification With Metagraph Representations, Sezin Kircali Ata, Yuan Fang, Min Wu, Xiao-Li Li, Xiaokui Xiao
Research Collection School Of Computing and Information Systems
This chapter is based on exploiting the network-based representations of proteins, metagraphs, in protein-protein interaction network to identify candidate disease-causing proteins. Protein-protein interaction (PPI) networks are effective tools in studying the functional roles of proteins in the development of various diseases. However, they are insufficient without the support of additional biological knowledge for proteins such as their molecular functions and biological processes. To enhance PPI networks, we utilize biological properties of individual proteins as well. More specifically, we integrate keywords from UniProt database describing protein properties into the PPI network and construct a novel heterogeneous PPI-Keyword (PPIK) network consisting …