Sentiment classification using statistical data compression models

With growing availability and popularity of user generated content, the discipline of sentiment analysis has come to the attention of many researchers. Existing work has mainly focused on either knowledge based methods or standard machine learning techniques. In this paper we investigate sentiment polarity classification based on adaptive statistical data compression models. We evaluate the classification performance of the lossless compression algorithm Prediction by Partial Matching (PPM) as well as compression based measures using PPM-like character n-gram frequency statistics. Comprehensive experiments on three corpora show that compression based methods are efficient, easy to apply and can compete with the accuracy of sophisticated classifiers such as support vector machines

Environmental change and migration in coastal regions: examples from Ghana and Indonesia

Coastal regions worldwide have been focal points for migration as well as affected by environmental changes for a long time. In the debate on climate change and migration coastal regions are among the “hot spot” areas that are supposed to be prone to “climate migration” in the near future. The paper analyses the situation in two different regional settings and advocates for a sound regional perspective on the relationship of environmental change and migration. Based on the conceptual framework of migrant trajectories, the paper shows how popu­lations in Keta (Ghana) and Semarang (Indonesia), affected by similar environmental changes such as flooding and erosion, react quite differently in terms of migration and mobility. The regional perspective as well as each region’s past experiences with migration and environmental changes shows to be crucial in order to understand current reactions to environmental degradation. The Keta setting represents a typology that pronounces migration trajectories as part of long-standing interregional and international migration, the Semarang setting, however, may be classified as a rather typical modernization-induced migration scheme, linked to rapidly growing urbanisation, with “trapped populations” on the one hand and in-migration of migrant workers on the other hand

Sentiment classification using statistical data compression models

With growing availability and popularity of user generated content, the discipline of sentiment analysis has come to the attention of many researchers. Existing work has mainly focused on either knowledge based methods or standard machine learning techniques. In this paper we investigate sentiment polarity classification based on adaptive statistical data compression models. We evaluate the classification performance of the lossless compression algorithm Prediction by Partial Matching (PPM) as well as compression based measures using PPM-like character n-gram frequency statistics. Comprehensive experiments on three corpora show that compression based methods are efficient, easy to apply and can compete with the accuracy of sophisticated classifiers such as support vector machines

Factors affecting student adoption of microblogging tools: A test of competing models

As new technologies develop, educators explore the feasibility of using these technologies to improve learning. Micro-blogging is one such technology. Given the propensity of students to use micro-blogging sites such as Twitter, it is important to understand whether these technologies would be adopted by students for educational use. This study explores whether students will adopt twitter as a supplemental learning tool. The Technology Acceptance Model (TAM) has be used extensively to understand adoption but it has been criticized for being too parsimonious to generalize across gender and culture. An alternate model, the Unified Theory of Acceptance and Use of Technology (UTAUT) has since been proposed to address this failing. Hence, in this study, two competing models were tested using two samples: a sample of US students and a sample of female Qatari students. Hence, the study will not only inform practice by providing information about a potential educational delivery method, it will provide additional evidence on the generalizability of studies based on these two models of technology adoption

An Empirical Study of the Relationship Between IT Infastructure Flexibility and IT Responsiveness in SMEs: A Resource-Based Analysis

For SMEs, responsiveness to changes is a critical survival capability. Practitioners believe that IT infrastructure flexibility contributes to responsiveness, yet empirical evidence is sparse. The purpose of this research is to empirically test the relationship between IT infrastructure flexibility, which is measured on four dimensions, and IT responsiveness. Two research questions are answered by this study: first, are all the flexibility factors needed to achieve IT responsiveness and second, how does each IT flexibility dimension affect IT responsiveness. According to resource-based theory, we speculate that among the four IT flexibility dimensions, only modularity and IT personnel competency have direct impact on IT responsiveness. Industrial data were collected and analyzed using PLS. The findings support our hypotheses

Promoting Learning Through Explainable Artificial Intelligence: An Experimental Study in Radiology

The deployment of machine learning (ML)-based decision support systems (DSSs) in high-risk environments such as radiology is increasing. Despite having achieved high decision accuracy, they are prone to errors. Thus, they are primarily used to assist radiologists in their decision making. However, collaborative decision making poses risks to the decision maker, e.g. automation bias and long-term performance degradation. To address these issues, we propose combining findings of the research streams of explainable artificial intelligence and education to promote human learning through interaction with ML-based DSSs. We provided radiologists with explainable vs non-explainable decision support that was high- vs low-performing in a between-subject experimental study to support manual segmentation of 690 brain tumor scans. Our results show that explainable ML-based DSSs improved human learning outcomes and prevented false learning triggered by incorrect decision support. In fact, radiologists were able to learn from errors made by the low-performing explainable ML-based DSS