558 research outputs found

    Mining Social Media and Structured Data in Urban Environmental Management to Develop Smart Cities

    Get PDF
    This research presented the deployment of data mining on social media and structured data in urban studies. We analyzed urban relocation, air quality and traffic parameters on multicity data as early work. We applied the data mining techniques of association rules, clustering and classification on urban legislative history. Results showed that data mining could produce meaningful knowledge to support urban management. We treated ordinances (local laws) and the tweets about them as indicators to assess urban policy and public opinion. Hence, we conducted ordinance and tweet mining including sentiment analysis of tweets. This part of the study focused on NYC with a goal of assessing how well it heads towards a smart city. We built domain-specific knowledge bases according to widely accepted smart city characteristics, incorporating commonsense knowledge sources for ordinance-tweet mapping. We developed decision support tools on multiple platforms using the knowledge discovered to guide urban management. Our research is a concrete step in harnessing the power of data mining in urban studies to enhance smart city development

    Content Analysis of Social Media: Public and Government Response to COVID-19 Pandemic in Indonesia

    Get PDF
    Nowadays, the use of social media to analyze disaster responses has become important. However, its application to support decision-making by the Government during disasters still present significant challenges. This article offers a complete analysis of the response of the public and the Government in dealing with the COVID-19 Pandemics in Indonesia. The content analysis uses to analyze the tweet post on Twitter to determine the public and government response. Data was collected from public and government tweets on Twitter and producing 11,578 community tweets from the public and 958 tweets from the government account. This data was collected from 2nd March until 15th April 2020. Public comments are sorted into six categories of comments, that is fate, logic, government mention, worry, scientist, and impression, while sentiments are classified as positive, negative, and neutral. Government comments are sorted into eight categories, namely information, education, operating, warnings, resources provision, volunteer recruitment, and rumors management. The results showed that the public encourages and supports the Government to cope with a pandemic think rationally and logically in dealing with this Pandemic. In addition, the study indicates that the Government has not used social media as a medium for communicating with the public. The quality of government response is not good, especially in the categories of information on operations, warnings, resources provision, recruitment of volunteers, and rumors management. The implication of this study suggests how the data might be useful for the Government in delivering information during the Pandemic

    Framing Twitter Public Sentiment on Nigerian Government COVID-19 Palliatives Distribution Using Machine Learning

    Get PDF
    Sustainable development plays a vital role in information and communication technology. In times of pandemics such as COVID-19, vulnerable people need help to survive. This help includes the distribution of relief packages and materials by the government with the primary objective of lessening the economic and psychological effects on the citizens affected by disasters such as the COVID-19 pandemic. However, there has not been an efficient way to monitor public funds’ accountability and transparency, especially in developing countries such as Nigeria. The understanding of public emotions by the government on distributed palliatives is important as it would indicate the reach and impact of the distribution exercise. Although several studies on English emotion classification have been conducted, these studies are not portable to a wider inclusive Nigerian case. This is because Informal Nigerian English (Pidgin), which Nigerians widely speak, has quite a different vocabulary from Standard English, thus limiting the applicability of the emotion classification of Standard English machine learning models. An Informal Nigerian English (Pidgin English) emotions dataset is constructed, pre-processed, and annotated. The dataset is then used to classify five emotion classes (anger, sadness, joy, fear, and disgust) on the COVID-19 palliatives and relief aid distribution in Nigeria using standard machine learning (ML) algorithms. Six ML algorithms are used in this study, and a comparative analysis of their performance is conducted. The algorithms are Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM), Random Forest (RF), Logistics Regression (LR), K-Nearest Neighbor (KNN), and Decision Tree (DT). The conducted experiments reveal that Support Vector Machine outperforms the remaining classifiers with the highest accuracy of 88%. The “disgust” emotion class surpassed other emotion classes, i.e., sadness, joy, fear, and anger, with the highest number of counts from the classification conducted on the constructed dataset. Additionally, the conducted correlation analysis shows a significant relationship between the emotion classes of “Joy” and “Fear”, which implies that the public is excited about the palliatives’ distribution but afraid of inequality and transparency in the distribution process due to reasons such as corruption. Conclusively, the results from this experiment clearly show that the public emotions on COVID-19 support and relief aid packages’ distribution in Nigeria were not satisfactory, considering that the negative emotions from the public outnumbered the public happiness

    Analysis and assessment of a knowledge based smart city architecture providing service APIs

    Get PDF
    Abstract The main technical issues regarding smart city solutions are related to data gathering, aggregation, reasoning, data analytics, access, and service delivering via Smart City APIs (Application Program Interfaces). Different kinds of Smart City APIs enable smart city services and applications, while their effectiveness depends on the architectural solutions to pass from data to services for city users and operators, exploiting data analytics, and presenting services via APIs. Therefore, there is a strong activity on defining smart city architectures to cope with this complexity, putting in place a significant range of different kinds of services and processes. In this paper, the work performed in the context of Sii-Mobility smart city project on defining a smart city architecture addressing a wide range of processes and data is presented. To this end, comparisons of the state of the art solutions of smart city architectures for data aggregation and for Smart City API are presented by putting in evidence the usage semantic ontologies and knowledge base in the data aggregation in the production of smart services. The solution proposed aggregate and re-conciliate data (open and private, static and real time) by using reasoning/smart algorithms for enabling sophisticated service delivering via Smart City API. The work presented has been developed in the context of the Sii-Mobility national smart city project on mobility and transport integrated with smart city services with the aim of reaching a more sustainable mobility and transport systems. Sii-Mobility is grounded on Km4City ontology and tools for smart city data aggregation, analytics support and service production exploiting smart city API. To this end, Sii-Mobility/Km4City APIs have been compared to the state of the art solutions. Moreover, the proposed architecture has been assessed in terms of performance, computational and network costs in terms of measures that can be easily performed on private cloud on premise. The computational costs and workloads of the data ingestion and data analytics processes have been assessed to identify suitable measures to estimate needed resources. Finally, the API consumption related data in the recent period are presented

    DomainSenticNet: An Ontology and a Methodology Enabling Domain-aware Sentic Computing

    Full text link
    [EN] In recent years, SenticNet and OntoSenticNet have represented important developments in the novel interdisciplinary field of research known as sentic computing, enabling the development of a variety of Sentic applications. In this paper, we propose an extension of the OntoSenticNet ontology, named DomainSenticNet, and contribute an unsupervised methodology to support the development of domain-aware Sentic applications. We developed an unsupervised methodology that, for each concept in OntoSenticNet, mines semantically related concepts from WordNet and Probase knowledge bases and computes domain distributional information from the entire collection of Kickstarter domain-specific crowdfunding campaigns. Subsequently, we applied DomainSenticNet to a prototype tool for Kickstarter campaign authoring and success prediction, demonstrating an improvement in the interpretability of sentiment intensities. DomainSenticNet is an extension of the OntoSenticNet ontology that integrates each of the 100,000 concepts included in OntoSenticNet with a set of semantically related concepts and domain distributional information. The defined unsupervised methodology is highly replicable and can be easily adapted to build similar domain-aware resources from different domain corpora and external knowledge bases. Used in combination with OntoSenticNet, DomainSenticNet may favor the development of novel hybrid aspect-based sentiment analysis systems and support further research on sentic computing in domain-aware applications.The work of Paolo Rosso was partially funded by the Spanish MICINN under the project PGC2018-096212-B-C31.Distante, D.; Faralli, S.; Rittinghaus, S.; Rosso, P.; Samsami, N. (2022). DomainSenticNet: An Ontology and a Methodology Enabling Domain-aware Sentic Computing. Cognitive Computation. 14(1):62-77. https://doi.org/10.1007/s12559-021-09825-w627714

    The Palgrave Handbook of Digital Russia Studies

    Get PDF
    This open access handbook presents a multidisciplinary and multifaceted perspective on how the ‘digital’ is simultaneously changing Russia and the research methods scholars use to study Russia. It provides a critical update on how Russian society, politics, economy, and culture are reconfigured in the context of ubiquitous connectivity and accounts for the political and societal responses to digitalization. In addition, it answers practical and methodological questions in handling Russian data and a wide array of digital methods. The volume makes a timely intervention in our understanding of the changing field of Russian Studies and is an essential guide for scholars, advanced undergraduate and graduate students studying Russia today

    The Palgrave Handbook of Digital Russia Studies

    Get PDF
    This open access handbook presents a multidisciplinary and multifaceted perspective on how the ‘digital’ is simultaneously changing Russia and the research methods scholars use to study Russia. It provides a critical update on how Russian society, politics, economy, and culture are reconfigured in the context of ubiquitous connectivity and accounts for the political and societal responses to digitalization. In addition, it answers practical and methodological questions in handling Russian data and a wide array of digital methods. The volume makes a timely intervention in our understanding of the changing field of Russian Studies and is an essential guide for scholars, advanced undergraduate and graduate students studying Russia today

    Data analytics 2016: proceedings of the fifth international conference on data analytics

    Get PDF

    Nowcasting user behaviour with social media and smart devices on a longitudinal basis: from macro- to micro-level modelling

    Get PDF
    The adoption of social media and smart devices by millions of users worldwide over the last decade has resulted in an unprecedented opportunity for NLP and social sciences. Users publish their thoughts and opinions on everyday issues through social media platforms, while they record their digital traces through their smart devices. Mining these rich resources offers new opportunities in sensing real-world events and indices (e.g., political preference, mental health indices) in a longitudinal fashion, either at the macro (population)-, or at the micro(user)-level. The current project aims at developing approaches to “nowcast" (predict the current state of) such indices at both levels of granularity. First, we build natural language resources for the static tasks of sentiment analysis, emotion disclosure and sarcasm detection over user-generated content. These are important for opinion monitoring on a large scale. Second, we propose a general approach that leverages textual data derived from generic social media streams to nowcast political indices at the macro-level. Third, we leverage temporally sensitive and asynchronous information to nowcast the political stance of social media users, at the micro-level using multiple kernel learning. We then focus further on the micro-level modelling, to account for heterogeneous data sources, such as information derived from users' smart phones, SMS and social media messages, to nowcast time-varying mental health indices of a small cohort of users on a longitudinal basis. Finally, we present the challenges faced when applying such micro-level approaches in a real-world setting and propose directions for future research
    corecore