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Research Collection School Of Computing and Information Systems

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Articles 5131 - 5160 of 8481

Full-Text Articles in Computer Sciences

Modeling Autobiographical Memory In Human-Like Autonomous Agents, Di Wang, Ah-Hwee Tan, Chunyan Miao May 2016

Modeling Autobiographical Memory In Human-Like Autonomous Agents, Di Wang, Ah-Hwee Tan, Chunyan Miao

Research Collection School Of Computing and Information Systems

Although autobiographical memory is an important part of the human mind, there has been little effort on modeling autobiographical memory in autonomous agents. With the motivation of developing human-like intelligence, in this paper, we delineate our approach to enable an agent to maintain memories of its own and to wander in mind. Our model, named Autobiographical Memory-Adaptive Resonance Theory network (AM-ART), is designed to capture autobiographical memories, comprising pictorial snapshots of one’s life experiences together with the associated context, namely time, location, people, activity, and emotion. In terms of both network structure and dynamics, AM-ART coincides with the autobiographical memory …

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#Greysanatomy Vs. #Yankees: Demographics And Hashtag Use On Twitter, Jisun An, Ingmar Weber May 2016

#Greysanatomy Vs. #Yankees: Demographics And Hashtag Use On Twitter, Jisun An, Ingmar Weber

Research Collection School Of Computing and Information Systems

Demographics, in particular, gender, age, and race, are a key predictor of human behavior. Despite the significant effect that demographics plays, most scientific studies using online social media do not consider this factor, mainly due to the lack of such information. In this work, we use state-of-the-art face analysis software to infer gender, age, and race from profile images of 350K Twitter users from New York. For the period from November 1, 2014 to October 31, 2015, we study which hashtags are used by different demographic groups. Though we find considerable overlap for the most popular hashtags, there are also …

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A Graph-Based Semantics Workbench For Concurrent Asynchronous Programs, Claudio Corrodi, Alexander Heußner, Christopher M. Poskitt Apr 2016

A Graph-Based Semantics Workbench For Concurrent Asynchronous Programs, Claudio Corrodi, Alexander Heußner, Christopher M. Poskitt

Research Collection School Of Computing and Information Systems

A number of novel programming languages and libraries have been proposed that offer simpler-to-use models of concurrency than threads. It is challenging, however, to devise execution models that successfully realise their abstractions without forfeiting performance or introducing unintended behaviours. This is exemplified by Scoop—a concurrent object-oriented message-passing language—which has seen multiple semantics proposed and implemented over its evolution. We propose a “semantics workbench” with fully and semi-automatic tools for Scoop, that can be used to analyse and compare programs with respect to different execution models. We demonstrate its use in checking the consistency of semantics by applying it to a …

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Olps: A Toolbox For On-Line Portfolio Selection, Bin Li, Doyen Sahoo, Hoi, Steven C. H. Apr 2016

Olps: A Toolbox For On-Line Portfolio Selection, Bin Li, Doyen Sahoo, Hoi, Steven C. H.

Research Collection School Of Computing and Information Systems

On-line portfolio selection is a practical financial engineering problem, which aims to sequentially allocate capital among a set of assets in order to maximize long-term return. In recent years, a variety of machine learning algorithms have been proposed to address this challenging problem, but no comprehensive open-source toolbox has been released for various reasons. This article presents the first open-source toolbox for "On-Line Portfolio Selection" (OLPS), which implements a collection of classical and state-of-the-art strategies powered by machine learning algorithms. We hope that OLPS can facilitate the development of new learning methods and enable the performance benchmarking and comparisons of …

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Not You Too? Distilling Local Contexts Of Poor Cellular Network Performance Through Participatory Sensing, Huiguang Liang, Ido Nevat, Hyong S. Kim, Hwee-Pink Tan, Wai-Leong Yeow Apr 2016

Not You Too? Distilling Local Contexts Of Poor Cellular Network Performance Through Participatory Sensing, Huiguang Liang, Ido Nevat, Hyong S. Kim, Hwee-Pink Tan, Wai-Leong Yeow

Research Collection School Of Computing and Information Systems

Cellular service subscribers are increasingly reliant on cellular data services for all kinds of mobile applications. Oftentimes, when subscribers experience frustratingly high network delays and timeouts, they like to know whether their experiences are shared by other users nearby. The question that is often asked is essentially this: “is it just me, or do others around me face the same problem?” In this paper, we describe how we use Tattle, a distributed real-time participatory sensing and monitoring framework, to glean network performance information from users nearby. Tattle relies on recent advances in peer-to-peer device networking, such as Wi-Fi Direct, Bluetooth …

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Tuning By Turning: Enabling Phased Array Signal Processing For Wifi With Inertial Sensors, Kun Qian, Chenshu Wu, Zheng Yang, Zimu Zhou, Xu Wang, Yunhao Liu Apr 2016

Tuning By Turning: Enabling Phased Array Signal Processing For Wifi With Inertial Sensors, Kun Qian, Chenshu Wu, Zheng Yang, Zimu Zhou, Xu Wang, Yunhao Liu

Research Collection School Of Computing and Information Systems

Modern mobile devices are equipped with multiple antennas, which brings various wireless sensing applications such as accurate localization, contactless human detection and wireless human-device interaction. A key enabler for these applications is phased array signal processing, especially Angle of Arrival (AoA) estimation. However, accurate AoA estimation on commodity devices is non-trivial due to limited number of antennas and uncertain phase offsets. Previous works either rely on elaborate calibration or involve contrived human interactions. In this paper, we aim to enable practical AoA measurements on commodity off-the-shelf (COTS) mobile devices. The key insight is to involve users’ natural rotation to formulate …

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What Makes A Music Track Popular In Online Social Networks?, Jing Ren, Jialie Shen, Robert John Kauffman Apr 2016

What Makes A Music Track Popular In Online Social Networks?, Jing Ren, Jialie Shen, Robert John Kauffman

Research Collection School Of Computing and Information Systems

Tens of thousands of music tracks are uploaded to the Internet every day through social networks that focus on music and videos, as well as portal websites. While some of the content has been popular for decades, some tracks that have just been released have been completely ignored. So what makes a music track popular? Can we predict the popularity of a music track before it is released? In this research, we will focus on an online music social network, Last.fm, and investigate three key factors of a music track that may have impact on its popularity. They include: the …

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On Effective Location-Aware Music Recommendation, Zhiyong Cheng, Jialie Shen Apr 2016

On Effective Location-Aware Music Recommendation, Zhiyong Cheng, Jialie Shen

Research Collection School Of Computing and Information Systems

Rapid advances in mobile devices and cloud-based music service now allow consumers to enjoy music any-time and anywhere. Consequently, there has been an increasing demand in studying intelligent techniques to facilitate context-aware music recommendation. However, one important context that is generally overlooked is user's venue, which often includes surrounding atmosphere, correlates with activities, and greatly influences the user's music preferences. In this article, we present a novel venue-aware music recommender system called VenueMusic to effectively identify suitable songs for various types of popular venues in our daily lives. Toward this goal, a Location-aware Topic Model (LTM) is proposed to (i) …

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Mapping Information Systems Student Skills To Industry Skills Framework, Shankararaman, Venky, Gottipati Swapna Apr 2016

Mapping Information Systems Student Skills To Industry Skills Framework, Shankararaman, Venky, Gottipati Swapna

Research Collection School Of Computing and Information Systems

SFIA skills framework is widely popular among education institutions and ICT industries. The framework provides ICT skills profiles which can be a valuable resource that supports the career planning of a student. However, currently a student does not have a method or approach to exploit the SFIA framework to align his or her competencies that he or she has acquired during the education program, to the skills defined in the SFIA framework. In this paper, we present a solution model for generating a skills report based on individual's competencies and experiences. In particular, we focus on Information Systems students' profiles. …

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Incentive Mechanism Design For Crowdsourcing: An All-Pay Auction Approach, Tie Luo, Sajal K. Das, Hwee-Pink Tan, Lirong Xia Apr 2016

Incentive Mechanism Design For Crowdsourcing: An All-Pay Auction Approach, Tie Luo, Sajal K. Das, Hwee-Pink Tan, Lirong Xia

Research Collection School Of Computing and Information Systems

Crowdsourcing can be modeled as a principal-agent problem in which the principal (crowdsourcer) desires to solicit maximal contribution from a group of agents (participants) while agents are only motivated to act to their own respective advantages. To reconcile this tension, we propose an all-pay auction approach to incentivize agents to act in the principal's interst, i.e., maximizing profit, while allowing agents to reap strictly positive utility. Our rationale for advocating all-pay auctions is based on two merits that we identify, namely all-pay auctions (i) compress the common, two-stage "bid-contribute" crowdsourcing process into a single "bid-cum-contribute" stage, and (ii) eliminate the …

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Personal Credit Profiling Via Latent User Behavior Dimensions On Social Media, Guangming Guo, Feida Zhu, Enhong Chen, Le Wu, Qi Liu, Yingling Liu, Minghui Qiu Apr 2016

Personal Credit Profiling Via Latent User Behavior Dimensions On Social Media, Guangming Guo, Feida Zhu, Enhong Chen, Le Wu, Qi Liu, Yingling Liu, Minghui Qiu

Research Collection School Of Computing and Information Systems

Consumer credit scoring and credit risk management have been the core research problem in financial industry for decades. In this paper, we target at inferring this particular user attribute called credit, i.e., whether a user is of the good credit class or not, from online social data. However, existing credit scoring methods, mainly relying on financial data, face severe challenges when tackling the heterogeneous social data. Moreover, social data only contains extremely weak signals about users’ credit label. To that end, we put forward a Latent User Behavior Dimension based Credit Model (LUBD-CM) to capture these small signals for personal …

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Understanding The Determinants Of Human Computation Game Acceptance: The Effects Of Aesthetic Experience And Output Quality, Xiaohui Wang, Dion Hoe-Lian Goh, Ee-Peng Lim, Wei Liang Adrian Vu Apr 2016

Understanding The Determinants Of Human Computation Game Acceptance: The Effects Of Aesthetic Experience And Output Quality, Xiaohui Wang, Dion Hoe-Lian Goh, Ee-Peng Lim, Wei Liang Adrian Vu

Research Collection School Of Computing and Information Systems

Purpose: Human computation games (HCGs) that blend gaming with utilitarian purposes are a potentially effective channel for content creation. The purpose of this paper is to investigate the driving factors behind players’ adoption of HCGs through a music video tagging game. The effects of perceived aesthetic experience (PAE) and perceived output quality (POQ) on HCG acceptance are empirically examined. Design/methodology/approach: An integrative structural model is developed to explain how hedonic and utilitarian factors, including PAE and POQ, working with another salient factor – perceived usefulness (PU) – affect the acceptance of HCGs. The structural equation modeling method is used to …

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Semantic Visualization With Neighborhood Graph Regularization, Tuan Minh Van Le, Hady W. Lauw Apr 2016

Semantic Visualization With Neighborhood Graph Regularization, Tuan Minh Van Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Visualization of high-dimensional data, such as text documents, is useful to map out the similarities among various data points. In the high-dimensional space, documents are commonly represented as bags of words, with dimensionality equal to the vocabulary size. Classical approaches to document visualization directly reduce this into visualizable two or three dimensions. Recent approaches consider an intermediate representation in topic space, between word space and visualization space, which preserves the semantics by topic modeling. While aiming for a good fit between the model parameters and the observed data, previous approaches have not considered the local consistency among data instances. We …

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Mercury Isotopes Of Atmospheric Particle Bound Mercury For Source Apportionment Study In Urban Kolkata, India, Reshmi Das, Xianfeng Wang, Bahareh Khezri, Richard D. Webster, Pradip Kumar Sikdar, Subhajit Datta Apr 2016

Mercury Isotopes Of Atmospheric Particle Bound Mercury For Source Apportionment Study In Urban Kolkata, India, Reshmi Das, Xianfeng Wang, Bahareh Khezri, Richard D. Webster, Pradip Kumar Sikdar, Subhajit Datta

Research Collection School Of Computing and Information Systems

The particle bound mercury (PBM) in urban-industrial areas is mainly of anthropogenic origin, and is derived from two principal sources: Hg bound to particulate matter directly emitted by industries and power generation plants, and adsorption of gaseous elemental mercury (GEM) and gaseous oxidized mercury (GOM) on air particulates from gas or aqueous phases. Here, we measured the Hg isotope composition of PBM in PM10 samples collected from three locations, a traffic junction, a waste incineration site and an industrial site in Kolkata, the largest metropolis in Eastern India. Sampling was carried out in winter and monsoon seasons between 2013–2015. …

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Smokey: Ubiquitous Smoking Detection With Commercial Wifi Infrastructures, Xiaolong Zheng, Jiliang Wang, Longfei Shangguan, Zimu Zhou, Yunhao Liu Apr 2016

Smokey: Ubiquitous Smoking Detection With Commercial Wifi Infrastructures, Xiaolong Zheng, Jiliang Wang, Longfei Shangguan, Zimu Zhou, Yunhao Liu

Research Collection School Of Computing and Information Systems

Even though indoor smoking ban is being put into practice in civilized countries, existing vision or sensor-based smoking detection methods cannot provide ubiquitous smoking detection. In this paper, we take the first attempt to build a ubiquitous passive smoking detection system, which leverages the patterns smoking leaves on WiFi signals to identify the smoking activity even in the non-line-of-sight and through-wall environments. We study the behaviors of smokers and leverage the common features to recognize the series of motions during smoking, avoiding the target-dependent training set to achieve the high accuracy. We design a foreground detection based motion acquisition method …

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When Peculiarity Makes A Difference: Object Characterisation In Heterogeneous Information Networks, Wei Chen, Feida Zhu, Lei Zhao, Xiaofang Zhou Apr 2016

When Peculiarity Makes A Difference: Object Characterisation In Heterogeneous Information Networks, Wei Chen, Feida Zhu, Lei Zhao, Xiaofang Zhou

Research Collection School Of Computing and Information Systems

A central task in heterogeneous information networks (HIN) is how to characterise an entity, which underlies a wide range of applications such as similarity search, entity profiling and linkage. Most existing work focus on using the main features common to all. While this approach makes sense in settings where commonality is of primary interest, there are many scenarios as important where uncommon and discriminative features are more useful. To address the problem, a novel model COHIN (Characterize Objects in Heterogeneous Information Networks) is proposed, where each object is characterized as a set of feature paths that contain both main and …

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Mycompetencies: Competency Tracking Mobile Application For Is Students, Gottipati Swapna, Shankararaman, Venky Apr 2016

Mycompetencies: Competency Tracking Mobile Application For Is Students, Gottipati Swapna, Shankararaman, Venky

Research Collection School Of Computing and Information Systems

The overall aim of learning outcomes and competency based education is to improve the efficiency and effectiveness of higher education. It's important to track regularly student's competency acquisition so that the faculty can improve the teaching delivery and adapt the content accordingly. Student based self-assessment of competency tracking on a weekly basis aids faculty to intervene in course delivery process for effective teaching and learning experience. It also helps students to have a better understanding of their competency levels and accordingly prepare for weekly sessions. To enable students to self-assess the competencies in more structured format, there is a need …

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Policy Analytics, Household Informedness And The Collection Of Household Hazardous Waste, Kustini Lim-Wavde, Robert J. Kauffman, Greg Dawson Apr 2016

Policy Analytics, Household Informedness And The Collection Of Household Hazardous Waste, Kustini Lim-Wavde, Robert J. Kauffman, Greg Dawson

Research Collection School Of Computing and Information Systems

Proper collection of Household Hazardous Waste (HHW) is an important action to support environmental sustainability. We investigate the role of household informedness, the degree to which households have the necessary information to make utility-maximizing decisions, as they relate to participation in HHW collection programs. We find two factors that influence household informedness: the provision of public education about HHW and environmental quality information. We conducted an empirical study on HHW collection in California to obtain statistical evidence on the effect of these factors on the amount of HHW collected. The findings of this policy analytics study improve our understanding of …

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Lightsense: Exploiting Smart Bulbs For Practical Multimodal Localization, Huynh Nguyen, Archan Misra, Youngki Lee Apr 2016

Lightsense: Exploiting Smart Bulbs For Practical Multimodal Localization, Huynh Nguyen, Archan Misra, Youngki Lee

Research Collection School Of Computing and Information Systems

Visible Light Positioning (VLP) is an exciting new approach but has to overcome considerable challenges before it becomes practical. In particular, the line-of-sight requirement which imports the constraint on the working condition of VLP and can not be solved by the VLP itself. This paper develops techniques to allow VLP to be augmented with traditional WI-FI approach by leveraging the WI-FI enabled smart bulbs which are now widely available. Those Smart bulbs offer several intriguing properties: a very dense deployment (roughly every 2 meters apart), ability to act as sensors that continually provide Wi-Fi signal strength measurements. We utilize these …

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Where Am I? Characterizing And Improving The Localization Performance Of Off-The-Shelf Mobile Devices Through Cooperation, Huiguang Liang, Hyong S. Kim, Hwee-Pink Tan, Wai-Leong Yeow Apr 2016

Where Am I? Characterizing And Improving The Localization Performance Of Off-The-Shelf Mobile Devices Through Cooperation, Huiguang Liang, Hyong S. Kim, Hwee-Pink Tan, Wai-Leong Yeow

Research Collection School Of Computing and Information Systems

We are increasingly reliant on cellular data services for many types of day-to-day activities, from hailing a cab, to searching for nearby restaurants. Geo-location has become a ubiquitous feature that underpins the functionality of such applications. Network operators can also benefit from accurate mobile terminal localization in order to quickly detect and identify location-related network performance issues, such as coverage holes and congestion, based on mobile measurements. Current implementations of mobile localization on the wildly-popular Android platform depend on either the Global Positioning System (GPS), Android's Network Location Provider (NLP), or a combination of both. In this paper, we extensively …

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Large Scale Online Kernel Learning, Jing Lu, Hoi, Steven C. H., Jialei Wang, Peilin Zhao, Zhi-Yong Liu Apr 2016

Large Scale Online Kernel Learning, Jing Lu, Hoi, Steven C. H., Jialei Wang, Peilin Zhao, Zhi-Yong Liu

Research Collection School Of Computing and Information Systems

In this paper, we present a new framework for large scale online kernel learning, making kernel methods efficient and scalable for large-scale online learning applications. Unlike the regular budget online kernel learning scheme that usually uses some budget maintenance strategies to bound the number of support vectors, our framework explores a completely different approach of kernel functional approximation techniques to make the subsequent online learning task efficient and scalable. Specifically, we present two different online kernel machine learning algorithms: (i) Fourier Online Gradient Descent (FOGD) algorithm that applies the random Fourier features for approximating kernel functions; and (ii) Nyström Online …

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Rate Distortion Balanced Data Compression In Wireless Sensor Networks, Mohammad Abu Alsheikh, Shaowei Lin, Dusit Niyato, Hwee-Pink Tan Apr 2016

Rate Distortion Balanced Data Compression In Wireless Sensor Networks, Mohammad Abu Alsheikh, Shaowei Lin, Dusit Niyato, Hwee-Pink Tan

Research Collection School Of Computing and Information Systems

This paper presents a data compression algorithm with error bound guarantee for wireless sensor networks (WSNs) using compressing neural networks. The proposed algorithm minimizes data congestion and reduces energy consumption by exploring spatio-temporal correlations among data samples. The adaptive rate-distortion feature balances the compressed data size (data rate) with the required error bound guarantee (distortion level). This compression relieves the strain on energy and bandwidth resources while collecting WSN data within tolerable error margins, thereby increasing the scale of WSNs. The algorithm is evaluated using real-world data sets and compared with conventional methods for temporal and spatial data compression. The …

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Crowdsourcing: A Building Block For Smart Cities, Archan Misra Apr 2016

Crowdsourcing: A Building Block For Smart Cities, Archan Misra

Research Collection School Of Computing and Information Systems

This talk will present a vision, and real-world examples, of the use of mobile crowdsourcing for building a variety of smart-city applications and services. I will first describe the paradigm of centrally-coordinated crowdsourcing, where the crowdsourcing platform intelligently recommends different tasks to different candidate workers, and contrast it with today's prevalent paradigm, where workers select and perform tasks in an uncoordinated, opportunistic fashion. I will then describe real-world examples of such crowdsourcing (and participatory sensing) for two applications: (a) smart campus monitoring and (b) last-mile urban logistics (package pickup and delivery). The talk will also describe the opportunities and open …

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Quality And Context-Aware Smart Health Care: Evaluating The Cost-Quality Dynamics, Nirmalya Roy, Christine Julien, Archan Misra, Sajal Das Apr 2016

Quality And Context-Aware Smart Health Care: Evaluating The Cost-Quality Dynamics, Nirmalya Roy, Christine Julien, Archan Misra, Sajal Das

Research Collection School Of Computing and Information Systems

Many emerging pervasive health-care applications require the determination of a variety of context attributes of an individual's activities and medical parameters and her surrounding environment. Context is a high-level representation of an entity's state, which captures activities, relationships, capabilities, etc. In practice, high-level context measures are often difficult to sense from a single data source and must instead be inferred using multiple sensors embedded in the environment. A key challenge in deploying context-driven health-care applications involves energy-efficient determination or inference of high-level context information from low-level sensor data streams. Because this abstraction has the potential to reduce the quality of …

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Ontology-Aided Feature Correlation For Multi-Modal Urban Sensing, Archan Misra, Zaman Lantra, Kasthuri Jayarajah Apr 2016

Ontology-Aided Feature Correlation For Multi-Modal Urban Sensing, Archan Misra, Zaman Lantra, Kasthuri Jayarajah

Research Collection School Of Computing and Information Systems

The paper explores the use of correlation across features extracted from different sensing channels to help in urban situational understanding. We use real-world datasets to show how such correlation can improve the accuracy of detection of city-wide events by combining metadata analysis with image analysis of Instagram content. We demonstrate this through a case study on the Singapore Haze. We show that simple ontological relationships and reasoning can significantly help in automating such correlation-based understanding of transient urban events.

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Dual-Server Public-Key Encryption With Keyword Search For Secure Cloud Storage, Rongmao Chen, Yi Mu, Guomin Yang, Fuchun Guo, Xiaofen Wang Apr 2016

Dual-Server Public-Key Encryption With Keyword Search For Secure Cloud Storage, Rongmao Chen, Yi Mu, Guomin Yang, Fuchun Guo, Xiaofen Wang

Research Collection School Of Computing and Information Systems

Searchable encryption is of increasing interest for protecting the data privacy in secure searchable cloud storage. In this paper, we investigate the security of a well-known cryptographic primitive, namely, public key encryption with keyword search (PEKS) which is very useful in many applications of cloud storage. Unfortunately, it has been shown that the traditional PEKS framework suffers from an inherent insecurity called inside keyword guessing attack (KGA) launched by the malicious server. To address this security vulnerability, we propose a new PEKS framework named dual-server PEKS (DS-PEKS). As another main contribution, we define a new variant of the smooth projective …

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Anonymous Proxy Signature With Hierarchical Traceability, Jiannan Wei, Guomin Yang, Yi Mu, Kaitai Liang Apr 2016

Anonymous Proxy Signature With Hierarchical Traceability, Jiannan Wei, Guomin Yang, Yi Mu, Kaitai Liang

Research Collection School Of Computing and Information Systems

Anonymous proxy signatures are very useful in the construction of anonymous credential systems such as anonymous voting and anonymous authentication protocols. As a basic requirement, we should ensure an honest proxy signer is anonymous. However, in order to prevent the proxy signer from abusing the signing right, we should also allow dishonest signers to be traced. In this paper, we present three novel anonymous proxy signature schemes with different levels of (namely, public, internal and original signer) traceability. We define the formal definitions and security models for these three different settings, and prove the security of our proposed schemes under …

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Foreword To Special Section On Graphics Interface 2015, Hao Zhang, Anthony Tang Apr 2016

Foreword To Special Section On Graphics Interface 2015, Hao Zhang, Anthony Tang

Research Collection School Of Computing and Information Systems

This special section of the Computers & Graphics (C&G) Journal features expanded versions of five of the top graphics and interactions papers [1–5] that were originally presented at Graphics Interface (GI) 2015, which took place in Halifax, Nova Scotia, Canada, between June 3rd and 5th. GI, sponsored by the Canadian Human–Computer Communications Society, is an annual international conference devoted to computer graphics and human– computer interaction (HCI). With a graphics track and an HCI track having equal weights in the conference, GI offers a unique venue for a meeting of minds working on computer graphics and interactive techniques. GI is …

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From Classification To Quantification In Tweet Sentiment Analysis, Wei Gao, Fabrizio Sebastiani Apr 2016

From Classification To Quantification In Tweet Sentiment Analysis, Wei Gao, Fabrizio Sebastiani

Research Collection School Of Computing and Information Systems

entiment classification has become a ubiquitous enabling technology in the Twittersphere, since classifying tweets according to the sentiment they convey towards a given entity (be it a product, a person, a political party, or a policy) has many applications in political science, social science, market research, and many others. In this paper, we contend that most previous studies dealing with tweet sentiment classification (TSC) use a suboptimal approach. The reason is that the final goal of most such studies is not estimating the class label (e.g., Positive, Negative, or Neutral) of individual tweets, but estimating the relative frequency (a.k.a. “prevalence”) …

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Persentiment: A Personalized Sentiment Classification System For Microblog Users, Kaisong Song, Ling Chen, Wei Gao, Shi Feng, Daling Wang, Chengqi Zhang Apr 2016

Persentiment: A Personalized Sentiment Classification System For Microblog Users, Kaisong Song, Ling Chen, Wei Gao, Shi Feng, Daling Wang, Chengqi Zhang

Research Collection School Of Computing and Information Systems

Microblogging services are playing increasingly important roles in our daily life today. It is useful for microblog users to instantly understand the sentiment of a large number of microblogs posted by their friends and make appropriate response. Despite considerable progress on microblog sentiment classification, most of the existing works ignore the influence of personal distinctions of different microblog users on the sentiments they convey, and none of them has provided real-world personalized sentiment classification systems. Considering personal distinctions in sentiment analysis is natural and necessary as different people have different language habits, personal characters, opinion bias and so on. In …

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