The Experts below are selected from a list of 429 Experts worldwide ranked by ideXlab platform
William A. Wallace - One of the best experts on this subject based on the ideXlab platform.
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HICSS - Automatic Text Analysis of Values in the Enron Email Dataset: Clustering a Social Network Using the Value Patterns of Actors
2010 43rd Hawaii International Conference on System Sciences, 2010Co-Authors: Yingjie Zhou, Kenneth R. Fleischmann, William A. WallaceAbstract:Widely discussed in the mass media, Web 2.0, or social software, has also drawn the attention of researchers, developing into a whole new research area. With Web 2.0's further development, corporations aim to adopt its technologies and transfer its benefits, such as enhanced collaboration and knowledge sharing, to their organizations. Whether any of these benefits also apply in an organizational conText and whether there are further, still uncovered, benefits remains unclear. Furthermore, research in this area is still in its early stages, thus hampering progress towards qualitative and quantitative models that could provide answers. In order to encourage further progress in this area, we reviewed the existing research on corporate blogging and identified 24 articles that investigate the topic. Using the framework by Ives et al. [18], we categorized the articles for further Analysis. By means of process theory, we build a conceptual model and identify the antecedents and consequences of internal corporate weblog usage. Our findings suggest that usage is driven by organizational culture, as well as by attitudes towards blogging. In addition, the benefits of weblog usage are centered on community benefits.
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Automatic Text Analysis of values in the enron email dataset: Clustering a social network using the value patterns of actors
Proceedings of the Annual Hawaii International Conference on System Sciences, 2010Co-Authors: Yingjie Zhou, Kenneth R. Fleischmann, William A. WallaceAbstract:Widely discussed in the mass media, Web 2.0, or social software, has also drawn the attention of researchers, developing into a whole new research area. With Web 2.0's further development, corporations aim to adopt its technologies and transfer its benefits, such as enhanced collaboration and knowledge sharing, to their organizations. Whether any of these benefits also apply in an organizational conText and whether there are further, still uncovered, benefits remains unclear. Furthermore, research in this area is still in its early stages, thus hampering progress towards qualitative and quantitative models that could provide answers. In order to encourage further progress in this area, we reviewed the existing research on corporate blogging and identified 24 articles that investigate the topic. Using the framework by Ives et al. [18], we categorized the articles for further Analysis. By means of process theory, we build a conceptual model and identify the antecedents and consequences of internal corporate weblog usage. Our findings suggest that usage is driven by organizational culture, as well as by attitudes towards blogging. In addition, the benefits of weblog usage are centered on community benefits.
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Automatic Text Analysis of Values in the Enron Email Dataset: Clustering a Social Network Using the Value Patterns of Actors
2010 43rd Hawaii International Conference on System Sciences, 2010Co-Authors: Yingjie Zhou, Kenneth R. Fleischmann, William A. WallaceAbstract:This paper describes an Automatic Text Analysis of values contained in the Enron email dataset that seeks to explore the potential to apply value patterns to cluster a social network. Two hypotheses are posed: individuals communicate more frequently with other individuals who share similar value patterns than with individuals with different value patterns; and people who communicate more frequently with each other share similar value patterns. The first hypothesis is supported: indeed, individuals were found to communicate more frequently with individuals who share similar value patterns, and further, the extent to which this is true appears to depend at least in part on the value patterns themselves. However, the second hypothesis is not supported - people who communicate more frequently with each other do not necessarily all fit into a particular value type. Thus, values have utility as a novel tool for social network Analysis.
Carlo Giovannella - One of the best experts on this subject based on the ideXlab platform.
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Smart City Analytics: state of the art and future perspectives
Interaction Design and Architecture(s), 2014Co-Authors: Carlo Giovannella, Mihai Dascalu, Federico ScacciaAbstract:In accordance with a "people centred" vision, this paper critically examines current approaches to smart cities benchmarking. In particular, by means of correlation Analysis and Principal Component Analysis (PCA) we put in evidence current limitations of city rankings and, as well, the emergence of different perspectives for data interpretations. To follow, a possible redesign of the "Smart Cities Analytics" grounded on the traces left by individuals, is suggested. In particular, as an example, we focus on the potential of Automatic Text Analysis tools to extract people perceptions and expectations that, in turns, demonstrate the need to integrate bottom-up and top-down approaches to city benchmarking. Finally a novel definition of smart city is proposed based on the territorial state of flow and, as a consequence, a novel path toward smart city benchmarking is brought forward.
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Monitoring Learning Experiences and Styles: The Socio-emotional Level
2010 10th IEEE International Conference on Advanced Learning Technologies, 2010Co-Authors: Chiara Spadavecchia, Carlo GiovannellaAbstract:This article describes the integration within an on-line learning environment - LIFE (Learning in an Interacting Framework to Experience) - of two tools that have been designed to allow "quasi-real-time" and "in situ" monitoring of complex educational "experiences/processes", thanks to the Analysis of the traces left by the learners. The two tools allow to monitor and analyze, respectively, the level of social interaction - through techniques of social network Analysis (SNA) - and the emotional state of learners through Automatic Text Analysis (ATA). When combined, the two tools allow also to monitor the emotional relationships and, ultimately, the 'social' emotional state of the community. As case history we present and discuss the results of a monitoring experiment conducted on an educational process which lasts five months (the second part of a master's degree in "E-Learning: methods, techniques and applications").
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ICALT - Monitoring Learning Experiences and Styles: The Socio-emotional Level
2010 10th IEEE International Conference on Advanced Learning Technologies, 2010Co-Authors: Chiara Spadavecchia, Carlo GiovannellaAbstract:This article describes the integration within an on-line learning environment - LIFE (Learning in an Interacting Framework to Experience) - of two tools that have been designed to allow "quasi-real-time" and "in situ" monitoring of complex educational "experiences/processes", thanks to the Analysis of the traces left by the learners. The two tools allow to monitor and analyze, respectively, the level of social interaction - through techniques of social network Analysis (SNA) - and the emotional state of learners through Automatic Text Analysis (ATA). When combined, the two tools allow also to monitor the emotional relationships and, ultimately, the 'social' emotional state of the community. As case history we present and discuss the results of a monitoring experiment conducted on an educational process which lasts five months (the second part of a master's degree in "E-Learning: methods, techniques and applications")
Yingjie Zhou - One of the best experts on this subject based on the ideXlab platform.
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HICSS - Automatic Text Analysis of Values in the Enron Email Dataset: Clustering a Social Network Using the Value Patterns of Actors
2010 43rd Hawaii International Conference on System Sciences, 2010Co-Authors: Yingjie Zhou, Kenneth R. Fleischmann, William A. WallaceAbstract:Widely discussed in the mass media, Web 2.0, or social software, has also drawn the attention of researchers, developing into a whole new research area. With Web 2.0's further development, corporations aim to adopt its technologies and transfer its benefits, such as enhanced collaboration and knowledge sharing, to their organizations. Whether any of these benefits also apply in an organizational conText and whether there are further, still uncovered, benefits remains unclear. Furthermore, research in this area is still in its early stages, thus hampering progress towards qualitative and quantitative models that could provide answers. In order to encourage further progress in this area, we reviewed the existing research on corporate blogging and identified 24 articles that investigate the topic. Using the framework by Ives et al. [18], we categorized the articles for further Analysis. By means of process theory, we build a conceptual model and identify the antecedents and consequences of internal corporate weblog usage. Our findings suggest that usage is driven by organizational culture, as well as by attitudes towards blogging. In addition, the benefits of weblog usage are centered on community benefits.
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Automatic Text Analysis of values in the enron email dataset: Clustering a social network using the value patterns of actors
Proceedings of the Annual Hawaii International Conference on System Sciences, 2010Co-Authors: Yingjie Zhou, Kenneth R. Fleischmann, William A. WallaceAbstract:Widely discussed in the mass media, Web 2.0, or social software, has also drawn the attention of researchers, developing into a whole new research area. With Web 2.0's further development, corporations aim to adopt its technologies and transfer its benefits, such as enhanced collaboration and knowledge sharing, to their organizations. Whether any of these benefits also apply in an organizational conText and whether there are further, still uncovered, benefits remains unclear. Furthermore, research in this area is still in its early stages, thus hampering progress towards qualitative and quantitative models that could provide answers. In order to encourage further progress in this area, we reviewed the existing research on corporate blogging and identified 24 articles that investigate the topic. Using the framework by Ives et al. [18], we categorized the articles for further Analysis. By means of process theory, we build a conceptual model and identify the antecedents and consequences of internal corporate weblog usage. Our findings suggest that usage is driven by organizational culture, as well as by attitudes towards blogging. In addition, the benefits of weblog usage are centered on community benefits.
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Automatic Text Analysis of Values in the Enron Email Dataset: Clustering a Social Network Using the Value Patterns of Actors
2010 43rd Hawaii International Conference on System Sciences, 2010Co-Authors: Yingjie Zhou, Kenneth R. Fleischmann, William A. WallaceAbstract:This paper describes an Automatic Text Analysis of values contained in the Enron email dataset that seeks to explore the potential to apply value patterns to cluster a social network. Two hypotheses are posed: individuals communicate more frequently with other individuals who share similar value patterns than with individuals with different value patterns; and people who communicate more frequently with each other share similar value patterns. The first hypothesis is supported: indeed, individuals were found to communicate more frequently with individuals who share similar value patterns, and further, the extent to which this is true appears to depend at least in part on the value patterns themselves. However, the second hypothesis is not supported - people who communicate more frequently with each other do not necessarily all fit into a particular value type. Thus, values have utility as a novel tool for social network Analysis.
Alexander Dunst - One of the best experts on this subject based on the ideXlab platform.
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How Good Is Good Enough? Establishing Quality Thresholds for the Automatic Text Analysis of Retro-Digitized Comics
MultiMedia Modeling, 2019Co-Authors: Rita Hartel, Alexander DunstAbstract:Stylometry in the form of simple statistical Text Analysis has proven to be a powerful tool for Text classification, e.g. in the form of authorship attribution. When analyzing retro-digitized comics, manga and graphic novels, the researcher is confronted with the problem that automated Text recognition (ATR) still leads to results that have comparatively high error rates, while the manual transcription of Texts remains highly time-consuming. In this paper, we present an approach and measures that specify whether stylometry based on unsupervised ATR will produce reliable results for a given dataset of comics images.
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MMM (2) - How Good Is Good Enough? Establishing Quality Thresholds for the Automatic Text Analysis of Retro-Digitized Comics
MultiMedia Modeling, 2018Co-Authors: Rita Hartel, Alexander DunstAbstract:Stylometry in the form of simple statistical Text Analysis has proven to be a powerful tool for Text classification, e.g. in the form of authorship attribution. When analyzing retro-digitized comics, manga and graphic novels, the researcher is confronted with the problem that automated Text recognition (ATR) still leads to results that have comparatively high error rates, while the manual transcription of Texts remains highly time-consuming. In this paper, we present an approach and measures that specify whether stylometry based on unsupervised ATR will produce reliable results for a given dataset of comics images.
Gabbi Kedma - One of the best experts on this subject based on the ideXlab platform.
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proactive screening for depression through metaphorical and Automatic Text Analysis
Artificial Intelligence in Medicine, 2012Co-Authors: Yair Neuman, Yohai Cohen, Dan Assaf, Gabbi KedmaAbstract:Objective: Proactive and Automatic screening for depression is a challenge facing the public health system. This paper describes a system for addressing the above challenge. Materials and method: The system implementing the methodology -Pedesis - harvests the Web for metaphorical relations in which depression is embedded and extracts the relevant conceptual domains describing it. This information is used by human experts for the construction of a ''depression lexicon''. The lexicon is used to Automatically evaluate the level of depression in Texts or whether the Text is dealing with depression as a topic. Results: Tested on three corpora of questions addressed to a mental health site the system provides 9% improvement in prediction whether the question is dealing with depression. Tested on a corpus of Blogs, the system provides 84.2% correct classification rate (p<.001) whether a post includes signs of depression. By comparing the system's prediction to the judgment of human experts we achieved an average 78% precision and 76% recall. Conclusion: Depression can be Automatically screened in Texts and the mental health system may benefit from this screening ability.
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Proactive screening for depression through metaphorical and Automatic Text Analysis
Artificial Intelligence in Medicine, 2012Co-Authors: Yair Neuman, Yohai Cohen, Dan Assaf, Gabbi KedmaAbstract:Objective: Proactive and Automatic screening for depression is a challenge facing the public health system. This paper describes a system for addressing the above challenge. Materials and method: The system implementing the methodology -Pedesis - harvests the Web for metaphorical relations in which depression is embedded and extracts the relevant conceptual domains describing it. This information is used by human experts for the construction of a ''depression lexicon''. The lexicon is used to Automatically evaluate the level of depression in Texts or whether the Text is dealing with depression as a topic. Results: Tested on three corpora of questions addressed to a mental health site the system provides 9% improvement in prediction whether the question is dealing with depression. Tested on a corpus of Blogs, the system provides 84.2% correct classification rate (p
