Selecting the individual numerical scale and prioritization method in the analytic hierarchy process : a 2-tuple fuzzy linguistic approach

The validity of the priority vector used in the analytic hierarchy process (AHP) relies on two factors: the selection of a numerical scale and the selection of a prioritization method. The traditional AHP selects only one numerical scale (e.g., the Saaty scale) and one prioritization method (e.g., the eigenvector method) for each particular problem. For this traditional selection approach, there is disagreement on which numerical scale and prioritization method is better in deriving a priority vector. In fact, the best numerical scale and the best prioritization method both rely on the content of the pairwise comparison data provided by the AHP decision makers. By defining a set of concepts regarding the scale function and the linguistic pairwise comparison matrices (LPCMs) of the priority vector and by using LPCMs to unify the format of the input and output of AHP, this paper extends the AHP prioritization process under the 2-tuple fuzzy linguistic model. Based on the extended AHP prioritization process, we present two performance measure criteria to evaluate the effect of the numerical scales and prioritization methods. We also use the performance measure criteria to develop a 2-tuple fuzzy linguistic multicriteria approach to select the best numerical scales and the best prioritization methods for different LPCMs. In this paper, we call this type of selection the individual selection of the numerical scale and prioritization method. We also compare this individual selection with traditional selection by using both random and real data and show better results with individual selection.

Prediction of age, sentiment, and connectivity from social media text

Social media corpora, including the textual output of blogs, forums, and messaging applications, provide fertile ground for linguistic analysis material diverse in topic and style, and at Web scale. We investigate manifest properties of textual messages, including latent topics, psycholinguistic features, and author mood, of a large corpus of blog posts, to analyze the impact of age, emotion, and social connectivity. These properties are found to be significantly different across the examined cohorts, which suggest discriminative features for a number of useful classification tasks. We build binary classifiers for old versus young bloggers, social versus solo bloggers, and happy versus sad posts with high performance. Analysis of discriminative features shows that age turns upon choice of topic, whereas sentiment orientation is evidenced by linguistic style. Good prediction is achieved for social connectivity using topic and linguistic features, leaving tagged mood a modest role in all classifications.

Linguistic and psychometric validation of the diabetes-specific quality-of-life scale in U. K. english for adults with type 1 diabetes

OBJECTIVE To develop a linguistically and psychometrically validated U.K. English (U.K./Ireland) version of the Diabetes-Specific Quality-of-Life Scale (DSQOLS) for adults with type 1 diabetes.

RESEARCH DESIGN AND METHODS We conducted independent forward and backward translation of the validated German DSQOLS. An iterative interview study with health professionals (n = 3) and adults with type 1 diabetes (n = 8) established linguistic validity. The DSQOLS was included in three Dose Adjustment for Normal Eating (DAFNE) studies (total N = 1,071). Exploratory factor analysis (EFA) was undertaken to examine questionnaire structure. Concurrent and discriminant validity, internal consistency, and reliability were assessed.

RESULTS EFA indicated a six-factor structure for the DSQOLS (social aspects, fear of hypoglycemia, dietary restrictions, physical complaints, anxiety about the future, and daily hassles). High internal consistency reliability was found for these factors and the weighted treatment satisfaction scale (α = 0.85–0.94). All subscales were moderately, positively correlated with the Audit of Diabetes-Dependent Quality-of-Life (ADDQoL) measure, demonstrating evidence of concurrent validity. Lower DSQOLS subscale scores [indicating impaired quality of life (QoL)] were associated with the presence of diabetes-related complications.

CONCLUSIONS The DSQOLS captures the impact of detailed aspects of modern type 1 diabetes management (e.g., carbohydrate counting and flexible insulin dose adjustment) that are now routine in many parts of the U.K. and Ireland. The U.K. English version of the DSQOLS offers a valuable tool for assessing the impact of treatment approaches on QoL in adults with type 1 diabetes.

Online social capital : mood, topical and psycholinguistic analysis

Social media provides rich sources of personal information and community interaction which can be linked to aspect of mental health. In this paper we investigate manifest properties of textual messages, including latent topics, psycholinguistic features, and authors' mood, of a large corpus of blog posts, to analyze the aspect of social capital in social media communities. Using data collected from Live Journal, we find that bloggers with lower social capital have fewer positive moods and more negative moods than those with higher social capital. It is also found that people with low social capital have more random mood swings over time than the people with high social capital. Significant differences are found between low and high social capital groups when characterized by a set of latent topics and psycholinguistic features derived from blogposts, suggesting discriminative features, proved to be useful for classification tasks. Good prediction is achieved when classifying among social capital groups using topic and linguistic features, with linguistic features are found to have greater predictive power than latent topics. The significance of our work lies in the importance of online social capital to potential construction of automatic healthcare monitoring systems. We further establish the link between mood and social capital in online communities, suggesting the foundation of new systems to monitor online mental well-being.

Community languages, linguistic isolation and social exclusion in the Australian context

The detrimental impacts of social exclusion to health and well-being are well-known and are of increasing concern around the world. For many of the population sub-groups who are most at risk of social exclusion, linguistic isolation—the inability to use and understand the majority language—is a major barrier to full participation in the life of the community as well as to full integration into the society in which its members live. This paper, using data obtained from community-based research in Melbourne, Australia, will discuss the problem of linguistic isolation in the context of Australian multicultural policy and use of languages other than English among members of culturally and linguistically diverse (CALD) communities. The experience of members of two specific CALD communities, speakers of Arabic and speakers of Indonesian, will be discussed to illustrate the impacts of linguistic isolation on health and well-being and to elucidate the relationship between CALD status and social exclusion in these communities.

Linguistic computational model based on 2-tuples and intervals

Herrera and Mart́inez initiated a 2-tuple fuzzy linguistic representation model for computing with words.Moreover, Wang and Hao further developed a new 2-tuple fuzzy linguistic representation model to deal with the linguistic term sets that are not uniformly and symmetrically distributed. This study proposes another linguistic computational model based on 2-tuples and intervals, which we call an interval version of the 2-tuple fuzzy linguistic representation model. The proposed model possesses three steps: 1) interval numerical scale; 2) computation based on interval numbers; and 3) a generalized inverse operation of the interval numerical scale. The first step transforms linguistic terms into interval numbers, based on which the second step is executed with output as an interval number. Finally, this number is then mapped into the interval of the linguistic 2-tuples by the generalized inverse operation. This study also generalizes the numerical scale approach, presented in the Wang and Hao model, to set the interval numerical scale, by considering the context where semantics of linguistic terms are defined by interval type-2 fuzzy sets (IT2 FSs). In order to compare the proposed model with the existing linguistic computational model based on IT2 FSs, we have conducted extensive simulations. The simulations demonstrate that the results obtained by our proposal are consistent with the results of the linguistic computational model based on IT2 FSs (in some sense) in a vast majority of cases.

Elaborativeness in academic writing. A study of three research papers

This paper investigates the employment of elaborative rhetorical strategies in threeresearch papers written in English and published in international sociologicaljournals: the first authored by native speakers of English, the second by a Polishwriter working in an Anglophone discourse community, and the third by a Polishwriter from the Polish discourse community. Elaboration relations are discussedwith respect to their textual function, frequency of employment, hierarchicallocation and recursiveness, and discoursal prominence. I explore how the authorselaborate their texts through amplification, extension, explanation, instantiation,reformulation and addition strategies. The analysis reveals that Elaboration is aprominent feature of the examined texts. It is proposed that the similarities inthe employment of Elaborations across the corpus result from the shared stylisticconventions and traditions of the disciplinary research community of sociologywhile variations in the mode of employment of elaborative structures may becaused by the writers’ differing linguistic backgrounds and discourse communitymemberships.

Effect of mood, social connectivity and age in online depression community via topic and linguistic analysis

Depression afflicts one in four people during their lives. Several studies have shown that for the isolated and mentally ill, the Web and social media provide effective platforms for supports and treatments as well as to acquire scientific, clinical understanding of this mental condition. More and more individuals affected by depression join online communities to seek for information, express themselves, share their concerns and look for supports [12]. For the first time, we collect and study a large online depression community of more than 12,000 active members from Live Journal. We examine the effect of mood, social connectivity and age on the online messages authored by members in an online depression community. The posts are considered in two aspects: what is written (topic) and how it is written (language style). We use statistical and machine learning methods to discriminate the posts made by bloggers in low versus high valence mood, in different age categories and in different degrees of social connectivity. Using statistical tests, language styles are found to be significantly different between low and high valence cohorts, whilst topics are significantly different between people whose different degrees of social connectivity. High performance is achieved for low versus high valence post classification using writing style as features. The finding suggests the potential of using social media in depression screening, especially in online setting.

Affective, linguistic and topic patterns in online autism communities

Online communities offer a platform to support and discuss health issues. They provide a more accessible way to bring people of the same concerns or interests. This paper aims to study the characteristics of online autism communities (called Clinical) in comparison with other online communities (called Control) using data from 110 Live Journal weblog communities. Using machine learning techniques, we comprehensively analyze these online autism communities. We study three key aspects expressed in the blog posts made by members of the communities: sentiment, topics and language style. Sentiment analysis shows that the sentiment of the clinical group has lower valence, indicative of poorer moods than people in control. Topics and language styles are shown to be good predictors of autism posts. The result shows the potential of social media in medical studies for a broad range of purposes such as screening, monitoring and subsequently providing supports for online communities of individuals with special needs.

Enhancing linguistic and cultural proficiency through Chinese children’s literature

This paper addresses the issue of selecting high-quality materials for teaching Chinese to non-native-speaker students. The paper argues that the unique nature of literary texts for children and adolescents written in simple and standard language reflecting the rich social fabric of China make them valuable materials for teaching foreign learners of the modern Chinese language. The special value of these materials to non-native learners lies not only in their linguistic aptness, but also in their informative connection between the modern Chinese language and the history and culture of China. The paper demonstrates how to effectively use these materials in a cooperative Chinese language classroom.

Graph-induced restricted Boltzmann machines for document modeling

Discovering knowledge from unstructured texts is a central theme in data mining and machine learning. We focus on fast discovery of thematic structures from a corpus. Our approach is based on a versatile probabilistic formulation – the restricted Boltzmann machine (RBM) –where the underlying graphical model is an undirected bipartite graph. Inference is efficient document representation can be computed with a single matrix projection, making RBMs suitable for massive text corpora available today. Standard RBMs, however, operate on bag-of-words assumption, ignoring the inherent underlying relational structures among words. This results in less coherent word thematic grouping. We introduce graph-based regularization schemes that exploit the linguistic structures, which in turn can be constructed from either corpus statistics or domain knowledge. We demonstrate that the proposed technique improves the group coherence, facilitates visualization, provides means for estimation of intrinsic dimensionality, reduces overfitting, and possibly leads to better classification accuracy.