Integrated microblog sentiment analysis from users’ social interaction patterns and textual opinions

Traditional post-level opinion classification methods usually fail to capture a person’s overall sentiment orientation toward a topic from his/her microblog posts published for a variety of themes related to that topic. One reason for this is that the sentiments connoted in the textual expressions of microblog posts are often obscure. Moreover, a person’s opinions are often influenced by his/her social network. This study therefore proposes a new method based on integrated information of microblog users’ social interactions and textual opinions to infer the sentiment orientation of a user or the whole group regarding a hot topic. A Social Opinion Graph (SOG) is first constructed as the data model for sentiment analysis of a group of microblog users who share opinions on a topic. This represents their social interactions and opinions. The training phase then uses the SOGs of training sets to construct Sentiment Guiding Matrix (SGM), representing the knowledge about the correlation between users’ sentiments, Textual Sentiment Classifier (TSC), and emotion homophily coefficients of the influence of various types of social interaction on users’ mutual sentiments. All of these support a high-performance social sentiment analysis procedure based on the relaxation labeling scheme. The experimental results show that the proposed method has better sentiment classification accuracy than the textual classification and other integrated classification methods. In addition, IMSA can reduce pre-annotation overheads and the influence from sampling deviation.     

An emotion-aware music recommender system: bridging the user’s interaction and music recommendation

Multimedia Tools and Applications - In emotion-aware music recommender systems, the user’s current emotion is identified and considered in recommending music to him. We have two motivations...     

Adaptation of an automotive dialogue system to users’ expertise and evaluation of the system

Spoken dialogue systems (SDSs) can be used to operate devices, e.g. in the automotive environment. People using these systems usually have different levels of experience. However, most systems do not take this into account. In this paper, we present a method to build a dialogue system in an automotive environment that automatically adapts to the user’s experience with the system. We implemented the adaptation in a prototype and carried out exhaustive tests. Our usability tests show that adaptation increases both user performance and user satisfaction. We describe the tests that were performed, and the methods used to assess the test results. One of these methods is a modification of PARADISE, a framework for evaluating the performance of SDSs [Walker MA, Litman DJ, Kamm CA, Abella A (Comput Speech Lang 12(3):317–347, 1998)]. We discuss its drawbacks for the evaluation of SDSs like ours, the modifications we have carried out, and the test results.

The effects of recommendations’ presentation on persuasion and satisfaction in a movie recommender system

The explosive growth of Internet applications and content, during the last decade, has revealed an increasing need for information filtering and recommendation. Most research in the area of recommendation systems has focused on designing and implementing efficient algorithms that provide accurate recommendations. However, the selection of appropriate recommendation content and the presentation of information are equally important in creating successful recommender applications. This paper addresses issues related to the presentation of recommendations in the movies domain. The current work reviews previous research approaches and popular recommender systems, and focuses on user persuasion and satisfaction. In our experiments, we compare different presentation methods in terms of recommendations’ organization in a list (i.e. top N-items list and structured overview) and recommendation modality (i.e. simple text, combination of text and image, and combination of text and video). The most efficient presentation methods, regarding user persuasion and satisfaction, proved to be the “structured overview” and the “text and video” interfaces, while a strong positive correlation was also found between user satisfaction and persuasion in all experimental conditions.     

A generative framework for fast urban labeling using spatial and temporal context

This paper introduces a multi-level classification framework for the semantic annotation of urban maps as provided by a mobile robot. Environmental cues are considered for classification at different scales. The first stage considers local scene properties using a probabilistic bag-of-words classifier. The second stage incorporates contextual information across a given scene (spatial context) and across several consecutive scenes (temporal context) via a Markov Random Field (MRF). Our approach is driven by data from an onboard camera and 3D laser scanner and uses a combination of visual and geometric features. By framing the classification exercise probabilistically we take advantage of an information-theoretic bail-out policy when evaluating class-conditional likelihoods. This efficiency, combined with low order MRFs resulting from our two-stage approach, allows us to generate scene labels at speeds suitable for online deployment. We demonstrate the virtue of considering such spatial and temporal context during the classification task and analyze the performance of our technique on data gathered over almost 17 km of track through a city.     

A framework for preservation of cloud users’ data privacy using dynamic reconstruction of metadata

In the rising paradigm of cloud computing, attainment of sustainable levels of cloud users’ trust in using cloud services is directly dependent on effective mitigation of its associated impending risks and resultant security threats. Among the various indispensible security services required to ensure effective cloud functionality leading to enhancement of users’ confidence in using cloud offerings, those related to the preservation of cloud users’ data privacy are significantly important and must be matured enough to withstand the imminent security threats, as emphasized in this research paper. This paper highlights the possibility of exploiting the metadata stored in cloud's database in order to compromise the privacy of users’ data items stored using a cloud provider's simple storage service. It, then, proposes a framework based on database schema redesign and dynamic reconstruction of metadata for the preservation of cloud users’ data privacy. Using the sensitivity parameterization parent class membership of cloud database attributes, the database schema is modified using cryptographic as well as relational privacy preservation operations. At the same time, unaltered access to database files is ensured for the cloud provider using dynamic reconstruction of metadata for the restoration of original database schema, when required. The suitability of the proposed technique with respect to private cloud environments is ensured by keeping the formulation of its constituent steps well aligned with the recommendations proposed by various Standards Development Organizations working in this domain.     

A decision system using fuzzy cognitive map and multi-group data envelopment analysis to estimate hospitals’ outputs level

Due to its vital role in healthcare system, performance evaluation of hospitals is indispensable. In addition, hospitals try to achieve desired and efficient conditions by careful planning based on their present facilities. Several studies have been conducted on hospital evaluation, but nearly none of them has taken into consideration the difference in the nature of performance in respect of hospitals’ managerial construction, funding, and type of services provided by them. Furthermore, hospitals’ outputs have not been estimated in respect of cause and effect relationships between inputs and outputs for achieving efficient conditions. In the present study, first, a new approach for hospital evaluation is presented according to the differences in the nature of their performances while categorized in groups. Then, optimal outputs for each hospital in its own group are dealt with using results obtained from multi-group data envelopment analysis and the method of fuzzy cognitive map. The activation Hebbian learning (AHL) algorithm is adapted to the concept of efficiency and is conducted to estimate the outputs of inefficient hospitals. In the present study, 27 hospitals located in the provincial capitals in northwest of Iran are categorized in four groups including general governmental hospitals, specialty governmental hospitals, private hospitals, and social security hospitals. Afterward, optimal outputs are estimated for the inefficient hospitals by using the proposed modified AHL algorithm. The results indicate that when the hospitals have been evaluated in groups, efficiency scores of hospitals have changed. Also, given the cause and effect relationships between inputs and outputs in each group can help to decision and policy makers to estimate the optimal outputs that have caused inefficient hospitals become efficient.

     

Rating prediction by exploring user’s preference and sentiment

With the development of e-commerce, shopping on-line is becoming more and more popular. The explosion of reviews have led to a serious problem, information overloading. How to mine user interest from these reviews and understand users’ preference is crucial for us. Traditional recommender systems mainly use structured data to mine user interest preference, such as product category, user’s tag, and the other social factors. In this paper, we firstly use LDA+Word2vec model to mine user interest. Then, we propose a social user sentimental measurement approach. At last, three factors, including user topic, user sentiment and interpersonal influence, are fused into a recommender system (RS) based on probabilistic matrix factorization. We conduct a series of experiments on Yelp dataset, and experimental results show the proposed approach outperforms the existing approaches.     

A pH measuring system using lotus measure™

An Orion SA 720 pH/ISE meter was interfaced to an IBM PC via the RS-232C interface. The laboratory software, Lotus Measure, was adopted for data acquisition and data analysis. Details of the interfacing hardware and software were given.     

The longitudinal impact of enterprise system users’ pre-adoption expectations and organizational support on post-adoption proficient usage

Although enterprise systems (ES) are ubiquitous, many firms report less than stellar payoffs from these costly investments, with underutilization often attributed to failures in the implementation process. Unfortunately, research has not provided sufficient insights into these failures, in part because it has focused on actual usage, as opposed to proficient usage, as the benchmark for successful implementation. Moreover, research has not generally examined how the adoption process unfolds over time, thus overlooking potential underlying mechanisms that may help explain how adopters achieve proficiency. To begin addressing these shortcomings, we study how adopters’ pre-adoption expectations, enacted over time, can influence their post-adoption proficiency, by shaping how and why they spend time using the system during the adoption period. We analyzed time-lagged survey data from 153 financial analysts, required to adopt new ES-based software, at a multinational bank. We found that adopters who hold pre-adoption expectations reflecting greater internal and external motives to adopt the system as well as systematically integrate it into their work routines are more apt to use the system in ways that enhance their cumulative knowledge of it, and subsequently develop higher levels of proficiency post-adoption. Moreover, greater organizational support enhanced the impact of adopters’ expectations on proficiency, except when their actual use is low in which case organizational support had an adverse effect.     

Spam detection on Twitter using a support vector machine and users’ features by identifying their interactions

Multimedia Tools and Applications - Spam tweets might cause numerous problems for users. An automatic method is introduced as a proposed method to detect spam tweets. This method is based on...     

A survey on managing users’ preferences in ambient intelligence

Universal Access in the Information Society - Understanding the importance of preference management in ambient intelligent environments is key to providing systems that are better prepared to meet...     

A novel recommendation approach based on users’ weighted trust relations and the rating similarities

With the growing popularity of open social networks, approaches incorporating social relationships into recommender systems are gaining momentum, especially matrix factorization-based ones. The experiments in previous literatures indicate that social information is very effective in improving the performance of traditional recommendation algorithms. However, most of existing social recommendation methods only take one kind of social relations—trust information into consideration, which is far from satisfactory. Furthermore, most of the existing trust networks are binary, which results in the equal treatment to different users who are trusted by the same user in these methods. In this paper, based on matrix factorization methods, we propose a new approach to make recommendation with social information. Its novelty can be summarized as follows: (1) it shows how to add different weights on the social trust relationships among users based on the trustee’s competence and trustworthiness; (2) it incorporates the similarity relationships among users as a complement into the social trust relationships to enhance the computation of user’s neighborhood; (3) it can balance the influence of these two kinds of relationships based on user’s individuality adaptively. Experiments on Epinions and Ciao datasets demonstrate that our approach outperforms the state-of-the-art algorithms in terms of mean absolute error and root mean square error, in particular for the users who rated a few items.     

Characterizing users’ check-in activities using their scores in a location-based social network

Analysis of users’ check-ins in location-based social networks (LBSNs, also called GeoSocial Networks), such as Foursquare and Yelp, is essential to understand users’ mobility patterns and behaviors. However, most empirical results of users’ mobility patterns reported in the current literature are based on users’ sampled and nonconsecutive public check-ins. Additionally, such analyses take no account of the noise or false information in the dataset, such as dishonest check-ins created by users. These empirical results may be biased and hence may bring side effects to LBSN services, such as friend and venue recommendations. Foursquare, one of the most popular LBSNs, provides a feature called a user’s score. A user’s score is an aggregate measure computed by the system based on more accurate and complete check-ins of the user. It reflects a snapshot of the user’s temporal and spatial patterns from his/her check-ins. For example, a high user score indicates that the user checked in at many venues regularly or s/he visited a number of new venues. In this paper, we show how a user’s score can be used as an alternative way to investigate the user’s mobility patterns. We first characterize a set of properties from the time series of a user’s consecutive weekly scores. Based on these properties, we identify different types of users by clustering users’ common check-in patterns using non-negative matrix factorization (NMF). We then analyze the correlations between the social features of user clusters and users’ check-in patterns. We present several interesting findings. For example, users with high scores (more mobile) tend to have more friends (more social). Our empirical results demonstrate how to uncover interesting spatio-temporal patterns by utilizing the aggregate measures released by a LBSN service.     

Optimal design and verification of temporal and spatial filters using second-order cone programming approach

11 Introduction The filters are widely used in many applications of signal processing. Filter design is an important research problem in many diverse application areas. The filters we usually refer to are temporal filters, which pass the frequency components of interest and attenuate the others. A spatial filter passes the signal radiating from a specific location and attenuates signals from other locations. Beamformer that widely used in radar, sonar,and wireless communications is a kind of …     

Hybrid credit ranking intelligent system using expert system and artificial neural networks

The main goal of all commercial banks is to collect the savings of legal and real persons and allocate them as credit to industrial, services and production companies. Non repayment of such credits cause many problems to the banks such as incapability to repay the central bank’s loans, increasing the amount of credit allocations comparing to credit repayment and incapability to allocate more credits to customers. The importance of credit allocation in banking industry and it’s important role in economic growth and employment creation leads the development of many models to evaluate the credit risk of applicants. But many of these models are classic and are incapable to do credit evaluation completely and efficiently. Therefore the demand to use artificial intelligence in this field has grown up. In this paper after providing appropriate credit ranking model and collecting expert’s knowledge, we design a hybrid intelligent system for credit ranking using reasoning-transformational models. Expert system as symbolic module and artificial neural network as non-symbolic module are components of this hybrid system. Such models provide the unique features of each components, the reasoning and explanation of expert system and the generalization and adaptability of artificial neural networks. The results of this system demonstrate hybrid intelligence system is more accurate and powerful in credit ranking comparing to expert systems and traditional banking models.     

Detecting and tagging users’ social circles in social media

As a media and communication platform, microblog becomes more popular around the world. Most users follow a large number of celebrities and public medias on microblog; however, these celebrities do not necessarily follow all their fans. Such one-way relationship abounds in ego network and is displayed by the forms of users’ followees and followers, which make it difficult to identify users’ real friends who are contained in merged lists of followees and followers. The aim of this paper is to propose a general algorithm for detecting users’ real friends in social media and dividing them into different social circles automatically according to the closeness of their relationships. Then we analyze these social circles and detect social attributes of these social circles. To verify the effectiveness of the proposed algorithm, we build a microblog application which displays algorithm results of social circles for users and enables users to adjust proposed results according to her/his real social circles. We demonstrate that our algorithm is superior to the traditional clustering method in terms of F value and mean average precision. Furthermore, our method of tagging social attributes of social circles gets high performance by NDCG (normalized discounted cumulative gain).     

Cognitive styles and users’ responses to structured information representation

Structured information repositories, such as digital libraries, Web directories, and subject gateways, require effective ways to organise and manage information. This paper focuses on Web directories and investigates the relationships between users cognitive styles and information representation. The results indicate that cognitive style influences participants reactions to the organisation of subject categories, presentation of the results, and screen layout. The findings are applied to develop a design framework that can support the improvement of Web directories and other structured digital information resources. Finally, implications for information representation are discussed.     

A study on the relationships between different presentation modes of graphical icons and users’ attention

With the vigorous development of applications (App), graphical user interfaces (GUI) have been widely found in computers and handheld devices. This study aimed to explore the relationships between different presentation modes of graphical icons and users’ attention. An eye tracker was employed to measure each participant’s experimental data; in addition, subjective evaluation of attention was conducted. Thus, the optimum presentation mode attracting the most attention might be determined. The experiment was designed to investigate two variables: icon composition and background. Through permutation and combination, six presentation modes were obtained as follows: line + positive background (M1), plane + positive background (M2), line + negative background (M3), plane + negative background (M4), line + no background (M5), and plane + no background (M6). Thirty-six participants were requested to view thirty stimuli, or the contour drawings of graphical icons, presented simultaneously in six modes mentioned above. The participants’ fixation duration, fixation frequency, and subjective evaluation of attention were analyzed through two-way ANOVA. The analytical results showed that in terms of the three performance indicators above, M4 performed the best among the six presentation modes. Moreover, regarding icon composition, planes performed better than lines in terms of the three performance indicators. As for background, negative background performed the best in terms of the three performance indicators, positive background ranked second, and no background performed the worst. The findings can serve as a reference when icons are researched or designed in the future.     

Exploring users’ experiences of using personal cloud storage services: a phenomenological study

Recently, cloud computing has attracted great attention for both personal and organisational usages. Despite a large number of research conducted about cloud adoption, little is known about how users practically experienced the usage and adoption of Personal Could Storage Service (PCSSs) particularly in the context of developing countries. To address this gap, this paper seeks a clear understanding of how participants have profoundly experienced and perceived PCSSs to obtain worthwhile insights towards the essence of PCSSs’ adoption as a multifaceted phenomenon. Applying the interpretive phenomenology as a research method, required data were collected by conducting semi-structured interviews with 12 experienced participants. The results reveal that users’ intentions to adopt PCSSs are highly influenced by three emerged themes, including demand-side factors, supply-side factors, and institutional factors. Concerning the demand-side factors, the paper demonstrates how user-centric aspects work in favour of PCSSs’ adoption. As to the supply-side factors, this research promotes an understanding of how the participants have perceived tangible and non-tangible features of PCSSs as a determinant of the adoption. Regarding the formal and informal institutions, remarkable roles of regulatory environments and culture are highlighted, respectively. The theoretical and practical implications of this study are discussed for both academia and practitioners.