Multiple-Domain Sentiment Classification for Cantonese Using a Combined Approach

In this study, we proposed a combined approach, which amalgamates machine learning and lexicon- based approaches for multiple-domain sentiment classification that supports Cantonese-based social media analysis. Our study contributes to the existing literature not only by investigating the effectiveness of the proposed combined approach for supporting social media analysis in the Cantonese context but also by verifying that the proposed method outperforms the baseline approaches, which are commonly used in the literature. We demonstrated that social media network-based classifiers can be general classifiers that support multiple-domain sentiment classification

MOFSocialNet: Exploiting Metal-Organic Framework Relationships via Social Network Analysis

The number of metal-organic frameworks (MOF) as well as the number of applications of this material are growing rapidly. With the number of characterized compounds exceeding 100,000, manual sorting becomes impossible. At the same time, the increasing computer power and established use of automated machine learning approaches makes data science tools available, that provide an overview of the MOF chemical space and support the selection of suitable MOFs for a desired application. Among the different data science tools, graph theory approaches, where data generated from numerous real-world applications is represented as a graph (network) of interconnected objects, has been widely used in a variety of scientific fields such as social sciences, health informatics, biological sciences, agricultural sciences and economics. We describe the application of a particular graph theory approach known as social network analysis to MOF materials and highlight the importance of community (group) detection and graph node centrality. In this first application of the social network analysis approach to MOF chemical space, we created MOFSocialNet. This social network is based on the geometrical descriptors of MOFs available in the CoRE-MOFs database. MOFSocialNet can discover communities with similar MOFs structures and identify the most representative MOFs within a given community. In addition, analysis of MOFSocialNet using social network analysis methods can predict MOF properties more accurately than conventional ML tools. The latter advantage is demonstrated for the prediction of gas storage properties, the most important property of these porous reticular network

A Social Network-Guided Approach to Machine Learning for Metal-Organic Framework Property Prediction

The number of new materials and applications of these materials is experiencing rapid growth. ‎Today, increased computational power and the established use of automated machine learning ‎approaches make data science tools available, which provide an overview of the chemical space, ‎support the choice of appropriate materials, and predict specific properties of materials for the ‎desired application. Among the different data science tools, graph theory approaches, where data ‎generated from numerous real-world applications are represented as a graph (network) of ‎connected objects, has been widely used in a variety of scientific fields such as social sciences, ‎health informatics, biological sciences, agricultural sciences, and economics. In this work, we ‎describe applying a particular graph theory approach, social network analysis (SNA), to the metal-organic framework (MOF). To demonstrate MOF materials, we construct a social network called ‎MOFSocialNet from geometrical MOFs descriptors in the CoRE-MOFs database. The MOFSocialNet ‎is an undirected, weighted, and heterogeneous social network; following the construction of this ‎graph, a set of social network analysis processes is conducted to extract valuable knowledge from ‎the MOFs data using graph machine learning algorithms. Community detection is one of the well-known SNA techniques employed on the MOFSocialNet to extract the most similar MOF ‎communities. To evaluate whether the properties of new MOFs can be predicted using MOF ‎communities, we randomly chose three from the CoRE MOFs database. For these MOFs, we ‎excluded the crystal density as input during featurization and placed the MOFs within the ‎MOFSocialNet. The crystal density of the new MOFs is predicted by simply averaging the crystal ‎density of the ten nearest neighbors. ‎ Additionally, communities extracted from MOFSocialNet can be leveraged to predict MOF gas ‎adsorption properties for CO2 and CH4.

SNA-Based Recommendation in Professional Learning Environments

Recommender systems can provide effective means to support self-organization and networking in professional learning environments. In this paper, we leverage social network analysis (SNA) methods to improve interest-based recommendation in professional learning networks. We discuss two approaches for interest-based recommendation using SNA and compare them with conventional collaborative filtering (CF)-based recommendation methods. The user evaluation results based on the ResQue framework confirm that SNA-based CF recommendation outperform traditional CF methods in terms of coverage and thus can provide an effective solution to the sparsity and cold start problems in recommender systems

Social Learning Analytics in Computer-Supported Collaborative Learning Environments: A Systematic Review of Empirical Studies

Social learning analytics (SLA) is a promising approach for identifying students’ social learning processes in computer-supported collaborative learning (CSCL) environments. To identify the main characteristics of SLA, gaps and future opportunities for this emerging approach, we systematically identified and analyzed 36 SLA-related studies conducted between 2011 and 2020. We focus on SLA implementation and methodological characteristics, educational focus, and the studies’ theoretical perspectives. The results show the predominance of SLA in formal and fully online settings with social network analysis (SNA) a dominant analytical technique. Most SLA studies aimed to understand students’ learning processes and applied the social constructivist perspective as a lens to interpret students’ learning behaviors. However, (i) few studies involve teachers in developing SLA tools, and rarely share SLA visualizations with teachers to support teaching decisions; (ii) some SLA studies are atheoretical; and (iii) the number of SLA studies integrating more than one analytical approach remains limited. Moreover, (iv) few studies leveraged innovative network approaches (e.g., epistemic network analysis, multimodal network analysis), and (v) studies rarely focused on temporal patterns of students’ interactions to assess how students’ social and knowledge networks evolve over time. Based on the findings and the gaps identified, we present methodological, theoretical and practical recommendations for conducting research and creating tools that can advance the field of SLA

A comparative study of russian trolls using several machine learning models on twitter data

Ever since Russian trolls have been brought into light, their interference in the 2016 US Presidential elections has been monitored and studied thoroughly. These Russian trolls have fake accounts registered on several major social media sites to influence public opinions. Our work involves trying to discover patterns in these tweets and classifying them by using different machine learning approaches such as Support Vector Machines, Word2vec and neural network models, and then creating a benchmark to compare all the different models. Two machine learning models are developed for this purpose. The first one is used to classify any given specific tweet as either troll or non-troll tweet. The second model classifies specific tweets as coming from left trolls or right trolls, based on apparent extreme political orientation. Several kinds of statistical analysis on these tweets are performed based on the tweets and their classifications. Further, an analysis of the machine learning algorithms, using several performance criteria, is presented

Through the wall of literacy: transformative practice in social networks among GCSE re-sit further education students

Purpose The purpose of this paper is to explain how peripheral participants contributed to and became more central members of a community of practice based in a social network that was used to support mobile learning approaches among post-compulsory education students. The notion was that in inducing participation through pedagogical strategies, individualised online presence could be increased that would support studentship, confidence and literacy improvements in participants who are normally apprehensive about online and formal learning contexts. Design/methodology/approach The network was used by four separate groups of 16-19 aged students and 19+ aged adults, with a constant comparison made of their activity and communication. A content analysis was made of students’ posts to the network, with the codes sorted thematically to examine how students used the network to support themselves and each other. Interviews were held with students across the two years to explore perceptions of the network and the community. Findings Peripheral participants navigate through ontological thresholds online to develop individual identity presence online. Increased communicated actions (“posts”) improves participation overall and the interaction of members in terms of developing a community of practice online. The results of communicated actions posted in visible online spaces improved the literacy control and willingness to publish content created by those peripheral participants. Research limitations/implications The study is taken from a small sample (approx. 100 students) in a case study comparing results across four different groups in an English Further Education college. Most of the positive results in terms of an impact being made on their literacy capability was found among adult students, as opposed to students in two 16-19 aged groups. Research implications identify hypothetical stages of identity presence online for reluctant and peripheral participants. This shows the potential of students to be induced to openly participate in visible contexts that can support further identity development. Practical implications The implications show that blended learning is necessary to improve the opportunity for mobile learning to happen. Blended learning in itself is dependent on and simultaneously improves group cohesion of learners in online communities. When students develop a momentum of engagement (and residence within) networks they exploit further technological features and functions and become more co-operative as a group, potentially reducing teacher presence. Learning activities need to support the peripheral participants in discrete and purposeful ways, usually achieved through personalised supported learning tasks. The notion and attention paid to the difficulties in bringing peripheral participants online has implications for the prescription of online learning as a form of delivery, especially among FE students. Social implications This paper problematizes the notion of peripheral participants and suggests they are overlooked in consideration of learning delivery, design and environments. Peripheral participants may be considered to be students who are at risk of not being involved in social organisations, such as communities, and vulnerable to diminished support, for instance through the withdrawal of face-to-face learning opportunities at the expense of online learning. Originality/value This paper makes a small contribution to theories surrounding communities of practice and online learning. By deliberately focusing on a population marginalised in current educational debate, it problematizes the growing prescription of online learning as a mode of delivery by taking the perspectives and experiences of peripheral participants on board

Comparison of Machine Learning Approaches on Arabic Twitter Sentiment Analysis

With the dramatic expansion of information over internet, users around the world express their opinion daily on the social network such as Facebook and Twitter. Large corporations nowadays invest on analyzing these opinions in order to assess their products or services by knowing the people feedback toward such business. The process of knowing users’ opinions toward particular product or services whether positive or negative is called sentiment analysis. Arabic is one of the common languages that have been addressed regarding sentiment analysis. In the literature, several approaches have been proposed for Arabic sentiment analysis and most of these approaches are using machine learning techniques. Machine learning techniques are various and have different performances. Therefore, in this study, we try to identifying a simple, but workable approach for Arabic sentiment analysis on Twitter. Hence, this study aims to investigate the machine learning technique in terms of Arabic sentiment analysis on Twitter. Three techniques have been used including Naïve Bayes, Decision Tree (DT) and Support Vector Machine (SVM). In addition, two simple sub-tasks pre-processing have been also used; Term Frequency-Inverse Document Frequency (TF-IDF) and Arabic stemming to get the heaviest weight term as the feature for tweet classification. TF-IDF aims to identify the most frequent words, whereas stemming aims to retrieve the stem of the word by removing the inflectional derivations. The dataset that has been used is Modern Arabic Corpus which consists of Arabic tweets. The performance of classification has been evaluated based on the information retrieval metrics precision, recall and f-measure. The experimental results have shown that DT has outperformed the other techniques by obtaining 78% of f-measure