From tags to emotions: Ontology-driven sentiment analysis in the social semantic web

Baldoni, Matteo; Baroglio, Cristina; Patti, Viviana; Rena, Paolo

doi:10.3233/ia-2012-0028

Cited by 54 publications

(32 citation statements)

References 8 publications

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“…Instead, analysis might reveal that positive and negative emotional statements have different effects on rumor-mongering, thus providing a basis for refining the theory. The outcome might be an ontology of: emotional statements, authenticating statements, interrogatory statements, prudent disclaimer statements, belief/disbelief statements, and work statements (Baldoni, Baroglio, Patti, & Rena, 2012). Within the ontology, different kinds of statements could be hypothesized to produce different effects on rumors.…”

Section: Examplementioning

confidence: 99%

Data-Driven Meets Theory-Driven Research in the Era of Big Data: Opportunities and Challenges for Information Systems Research

Maaß¹,

Parsons²,

Purao³

et al. 2018

JAIS

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The era of big data provides many opportunities for conducting impactful research from both datadriven and theory-driven perspectives. However, data-driven and theory-driven research have progressed somewhat independently. In this paper, we develop a framework that articulates important differences between these two perspectives and propose a role for information systems research at their intersection. The framework presents a set of pathways that combine the datadriven and theory-driven perspectives. From these pathways, we derive a set of challenges, and show how they can be addressed by research in information systems. By doing so, we identify an important role that information systems research can play in advancing both data-driven and theory-driven research in the era of big data. Medicine. His research investigates the relationship between conceptual modeling and AI/ML, AI in distributed environments (EdgeAI), and the design of smart services. Additionally he has a strong focus on knowledge transfer to industry through funded projects and spin-offs. 1273Carson Woo is Stanley Kwok Professor of Business, Sauder School of Business, University of British Columbia. His research interests include conceptual modeling, systems analysis and design, and requirements engineering. In particular, he is interested in using conceptual models to acquire relevant contextual information (e.g., business goals) and utilizing it to design new information systems, or aligning it to existing information systems design, so that changes can be more appropriate to business needs. At the University of British Columbia, he is a member of two research clusters: (1) Artificial Intelligence, and (2) Blockchain. Dr. Woo is editor of Information Technology and Systems Abstracts Journal at the Social Science Research Network, and serves or has served on several editorial boards, including ACM Transactions on Management Information Systems, Business & Information Systems Engineering Journal, Information and Management, and Requirements Engineering. He currently serves as the chair (2019-2020) of the Conceptual Modeling Conference steering committee and has served as the president of the

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Section: Examplementioning

confidence: 99%

Data-Driven Meets Theory-Driven Research in the Era of Big Data: Opportunities and Challenges for Information Systems Research

Maaß¹,

Parsons²,

Purao³

et al. 2018

JAIS

View full text Add to dashboard Cite

show abstract

“…For this it becomes necessary a supervised learning engine able of tolerating a significant level of noise in the training set, such as the progressive one. In (Baldoni et al, 2012), the idea is to use the "game with a purpose" paradigm as a source of crowdsourcing annotation, in which users, as a side effect of the game, perform the annotation work. This strategy goes alongside the more common one that exploits the famous Amazon Mechanical Turk crowdsourcing platform, where workers can be took as annotators.…”

Section: Related Workmentioning

confidence: 99%

Deep Convolutional Neural Networks for Sentiment Analysis Of Cultural Heritage

Paolanti

Pierdicca

Martini

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> The promotion of Cultural Heritage (CH) goods has become a major challenges over the last years. CH goods promote economic development, notably through cultural and creative industries and tourism. Thus, an effective planning of archaeological, cultural, artistic and architectural sites within the territory make CH goods easily accessible. A way of adding value to these services is making them capable of providing, using new technologies, a more immersive and stimulating fruition of information. In this light, an effective contribution can be provided by sentiment analysis. The sentiment related to a monument can be used for its evaluation considering that if it is positive, it influences its public image by increasing its value. This work introduces an approach to estimate the sentiment of Social Media pictures CH related. The sentiment of a picture is identified by an especially trained Deep Convolutional Neural Network (DCNN); aftewards, we compared the performance of three DCNNs: VGG16, ResNet and InceptionResNet. It is interesting to observe how these three different architectures are able to correctly evaluate the sentiment of an image referred to a ancient monument, historical buildings, archaeological sites, museum objects, and more. Our approach has been applied to a newly collected dataset of pictures from Instagram, which shows CH goods included in the UNESCO list of World Heritage properties.</p>

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“…Blogs (social web) and topic ontology (semantic web) are associated together to identify the tag which is recommended to the blog users. 65,86 The need of topic ontology in tag recommendation process is to provide conceptually relevant recommended tags to the content of the blog. Therefore, Topic ontology for a specific domain is efficiently constructed using online web resources, such as Wikipedia and WordNet, based on the relevant keywords and its lexical relationships.…”

Section: Association Between Social and Semantic Webmentioning

confidence: 99%

Data mining‐based tag recommendation system: an overview

Subramaniyaswamy

Varadharajan

Indragandhi

et al. 2015

WIREs Data Min & Knowl

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The advent of high‐speed Internet connections has revolutionized the way research is being carried out to obtain relevant information. Conversely, retrieving pertinent information from the copious resources available is not only difficult but also time consuming. In the recent years, tagging activity has been perceived as a potential source of knowledge on personal preferences, interests, targets, goals, and other attributes. Tags allow users to effectively annotate resources using keywords to personalize their recommendations and organize the resources for easy retrieval. However, the preference of users varies extremely resulting in tagging being counterproductive. These shortcomings reduce the application of the tagging system for filtering as well as retrieval of information. The tag recommendation system becomes useful by suggesting a set of relevant keywords to annotate the resources. This paper presents a review of the tag recommendation systems and the constraints that affects the available tag recommendation systems. Furthermore, we propose the use of spreading activation algorithm to study the role of constructed topic ontology for efficient tag recommendations. This approach is founded on the assumption that tags that are recommended to the user are predicted from the extracted keywords from the existing blogs and the topics in constructed topic ontology. We have also proposed a tag classification system, namely Correlation‐based Feature Selection–Hybrid Genetic Algorithm and classifier HGA‐SVM (support vector machine), and have compared the results with results produced by other existing feature selection methods. The results obtained from the experiments have been presented. WIREs Data Mining Knowl Discov 2015, 5:87–112. doi: 10.1002/widm.1149 This article is categorized under: Algorithmic Development > Web Mining Technologies > Classification Technologies > Computational Intelligence

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From tags to emotions: Ontology-driven sentiment analysis in the social semantic web

Cited by 54 publications

References 8 publications

Data-Driven Meets Theory-Driven Research in the Era of Big Data: Opportunities and Challenges for Information Systems Research

Data-Driven Meets Theory-Driven Research in the Era of Big Data: Opportunities and Challenges for Information Systems Research

Deep Convolutional Neural Networks for Sentiment Analysis Of Cultural Heritage

Data mining‐based tag recommendation system: an overview

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