There is a new generation of emoticons, called emojis, that is increasingly being used in mobile communications and social media. In the past two years, over ten billion emojis were used on Twitter. Emojis are Unicode graphic symbols, used as a shorthand to express concepts and ideas. In contrast to the small number of well-known emoticons that carry clear emotional contents, there are hundreds of emojis. But what are their emotional contents? We provide the first emoji sentiment lexicon, called the Emoji Sentiment Ranking, and draw a sentiment map of the 751 most frequently used emojis. The sentiment of the emojis is computed from the sentiment of the tweets in which they occur. We engaged 83 human annotators to label over 1.6 million tweets in 13 European languages by the sentiment polarity (negative, neutral, or positive). About 4% of the annotated tweets contain emojis. The sentiment analysis of the emojis allows us to draw several interesting conclusions. It turns out that most of the emojis are positive, especially the most popular ones. The sentiment distribution of the tweets with and without emojis is significantly different. The inter-annotator agreement on the tweets with emojis is higher. Emojis tend to occur at the end of the tweets, and their sentiment polarity increases with the distance. We observe no significant differences in the emoji rankings between the 13 languages and the Emoji Sentiment Ranking. Consequently, we propose our Emoji Sentiment Ranking as a European language-independent resource for automated sentiment analysis. Finally, the paper provides a formalization of sentiment and a novel visualization in the form of a sentiment bar.

Sentiment of Emojis.

Knowledge Work and Knowledge-Intensive Firms

Land use classification and change detection by using multi-temporal remotely sensed imagery: The case of Chunati wildlife sanctuary, Bangladesh

Enabling Medication Management Through Health Information Technology

How to accurately predict unknown quality-of-service (QoS) data based on observed ones is a hot yet thorny issue in Web service-related applications. Recently, a latent factor (LF) model has shown its efficiency in addressing this issue owing to its high accuracy and scalability. However, existing LF model-based QoS predictors mostly ignore the user/service neighborhoods, or reliability of given QoS data where noises commonly exist to cause accuracy loss. To address the above issues, this paper proposes a data-characteristic-aware latent factor (DCALF) model to implement highly accurate QoS predictions, where "data-characteristic-aware" indicates that it can appropriately implement QoS prediction according to the characteristics of given QoS data. Its main idea is two-fold: a) it detects the neighborhoods and noises of users and services based on the dense LFs extracted from the original sparse QoS data, b) it incorporates a density peaks-based clustering method into its modeling process for achieving the simultaneous detections of both neighborhoods and noises of QoS data. With such designs, it precisely represents the given QoS data in spite of their sparsity, thereby achieving highly accurate predictions for unknown ones. Results on two real datasets demonstrate that the proposed DCALF model outperforms state-of-the-art QoS predictors.

A Data-Characteristic-Aware Latent Factor Model for Web Services QoS Prediction

In this paper, an unsupervised segmentation methodology is proposed for remotely sensed images by using Fractional Differential FD based texture analysis model and Iterative Self-Organizing Data Analysis Technique Algorithm ISODATA. Essentially, image segmentation is used to assign unique class labels to different regions of an image. In this work, it is transformed into texture segmentation by signifying each class label as a unique texture class. The FD based texture analysis model is suggested for texture feature extraction from images and ISODATA is used for segmentation. The proposed methodology was first implemented on artificial target images and then on remote sensing images from Google Earth. The results of the proposed methodology are compared with those of the other texture analysis methods such as LBP Local Binary Pattern and NBP Neighbors based Binary Pattern by visual inspection as well as using classification measures derived from confusion matrix. It is justified that the proposed methodology outperforms LBP and NBP methods.

Unsupervised Segmentation of Remote Sensing Images using FD Based Texture Analysis Model and ISODATA

Semi-supervised change detection approach combining sparse fusion and constrained k means for multi-temporal remote sensing images

Learning style detection based on cognitive skills to support adaptive learning environment – A reinforcement approach

Traditionally the diagnosis of a disease is done by medical experts with experience, clinical data of the patients and adequate knowledge in identifying the disease. Such diagnosis is found to be approximate and time-consuming since it purely depends on the availability and the experience of the medical experts dealing with imprecise and uncertain clinical data of the patients. Hence, to improve decision making with uncertain data and to reduce the time consumption in diagnosing a disease, several simulated diagnosis systems have been developed. Most of these diagnosis systems are designed to possess the clinical data and symptoms associated with a specific disease as knowledge base. The quality of the knowledge base has an impact not only on the consequences, but also on the diagnostic precision. Most of the existing systems have been developed as an expert system that contains all the diagnosis facts as rules. Notably, applying the concept of a fuzzy set has shown better knowledge representation to improve the decision making process. Therefore an attempt is made in this paper to design and develop such diagnosis system, using a rough set. The system developed is evaluated using a simple set of symptoms that is added to clinical data in determining diabetes and its severity.

https://www.sciendo.com/pdf/10.2478/cait-2013-0030

Design of a Diabetic Diagnosis System Using Rough Sets

This paper is dedicated to the modelling of textured images influenced by fractional derivatives for texture detection. As most of the images contain textures, texture analysis becomes the most important for image understanding and it is a key solution for many computer vision applications. Hence, texture must be suitably detected and represented. Nevertheless, existing texture detection algorithms consider texture as a unique feature from edges. The proposed model explores a novel way of developing texture detection algorithm by mimicking edge detection algorithms. The method assumes that texture feature is analogous to edges and thus, the time complexity is reduced significantly. The model proposed in this work is based on Gaussian kernel smoothing, Fractional partial derivatives and a statistical approach. It is justified to be robust to noisy images and possesses statistical interpretation. The model is validated by the classification experiments on different types of textured images from Brodatz album. It achieves higher classification accuracy than the existing methods.

S. Margret Anouncia

Papers

Unsupervised Segmentation of Remote Sensing Images using FD Based Texture Analysis Model and ISODATA

Semi-supervised change detection approach combining sparse fusion and constrained k means for multi-temporal remote sensing images

Learning style detection based on cognitive skills to support adaptive learning environment – A reinforcement approach

Design of a Diabetic Diagnosis System Using Rough Sets

A Computational Model for Texture Analysis in Images with Fractional Differential Filter for Texture Detection