Emotional Concept Extraction Through Ontology-Enhanced Classification

Capturing emotions affecting human behavior in social media bears strategic importance in many decision-making fields, such as business and public policy, health care, and financial services, or just social events. This paper introduces an emotion-based classification model to analyze the human behavior in reaction to some event described by a tweet trend. From tweets analysis, the model extracts terms expressing emotions, and then, it builds a topological space of emotion-based concepts. These concepts enable the training of the multi-class SVM classifier to identify emotions expressed in the tweets. Classifier results are “softly” interpreted as a blending of several emotional nuances which thoroughly depicts people’s feeling. An ontology model captures the emotional concepts returned by classification, with respect to the tweet trends. The associated knowledge base provides human behavior analysis, in response to an event, by a tweet trend, by SPARQL queries. © 2019, Springer Nature Switzerland AG

Metadata and Semantic Research [electronic resource] : 13th International Conference, MTSR 2019, Rome, Italy, October 28–31, 2019, Revised Selected Papers /

This book constitutes the thoroughly refereed proceedings of the 13th International Conference on Metadata and Semantic Research, MTSR 2019, held in Rome, Italy, in October 2019. The 27 full and 15 short papers presented were carefully reviewed and selected from 96 submissions. The papers are organized in the following tracks: metadata and semantics for digital libraries, information retrieval, big, linked, social and open data; metadata and semantics for agriculture, food, and environment; digital humanities and digital curation; cultural collections and applications; european and national projects; metadata, identifiers and semantics in decentralized applications, blockchains and P2P systems.CLaRO: a controlled language for authoring competency questions -- Metadata-driven Semantic Coordination -- Training Biomedical Researchers in Medatata with a MIBBI-based ontology -- Emotional Concept Extraction through Ontology-enhanced classification -- A Classification of Grammar-infused Templates for Ontology and Model Verbalisation -- IoT Data Validation Using Spatial and Temporal Correlations -- Semantic Assistance System for Providing Smart Services and Reasoning in Aero-Engine Manufacturing -- Automated Subject Indexing of Domain Specific Collections Using Word Embeddings and General Purpose Thesauri -- An Ontology Based Approach for Data Leakage Prevention Against Advanced Persistent Threats -- Subtype Identification of Parkinson’s Disease Using Sparse Canonical Correlation and Clustering Analysis of Multimodal Neuroimaging -- Predictive Data Transformation Suggestions in Grafterizer Using Machine Learning -- Knowledge Graph Embeddings over Hundreds of Linked Datasets -- Changing the Subject: dynamic discussion monitoring in Twitter -- Proposal of a Metadata Application Profile for Technical Reports -- Applying Predictive Models to Support SKOS:ExactMatch Validation -- Ontology Based Anomaly Detection for File Integration -- Schema-based Visual Queries over Linked Data Endpoints -- Data Science from a Perspective of Computer Science -- Extending Faceted Search with Automated Object Ranking -- C-Rank: a concept linking approach to unsupervised keyphrase extraction -- Scholarly Resources Structuring: use cases for digital libraries -- OntoPPI: towards data formalization on the prediction of protein interactions -- Create Dashboards and Data & Analytics Frameworks._ Method for the Assessment of Semantic Accuracy Using Rules Identified by Conditional Functional Dependencies -- A Semantic Representation of the Citation Structure -- Hydrographic Datasets in Open Government Data Portals: mitigation of reusability issues through provenance documentation -- An Architecture for Enabling IoT Edge Devices to Allow Scalable Publishing of Semantic Linked Data -- Knowledge of Sustainable Water Stewardship -- Digital Transformation of Research Processes in the Humanities -- Curatr: a platform for semantic analysis and curation of historical literary texts -- Extraction of Character Profiles from the Gutenberg Archive -- Fine-GRAINed Process Metadata -- Formal Ontology in a Relativistic Setting -- A Research Toolbox: a complete suite for analysis in digital humanities -- The Null Result Portal -- Enriching Wikidata with Cultural Heritage Data from the COURAGE Project -- The Data Aggregation Lab Software: experimentation for linked data aggregation in cultural heritage -- Sinopia: a new linked-data editing environment designed for libraries -- Title Matching for Finding Identical Metadata Records in Different Language -- Metadata Integration with Labeled-property Graphs -- Timestamping Metadata Using Blockchain: a practical approach. .This book constitutes the thoroughly refereed proceedings of the 13th International Conference on Metadata and Semantic Research, MTSR 2019, held in Rome, Italy, in October 2019. The 27 full and 15 short papers presented were carefully reviewed and selected from 96 submissions. The papers are organized in the following tracks: metadata and semantics for digital libraries, information retrieval, big, linked, social and open data; metadata and semantics for agriculture, food, and environment; digital humanities and digital curation; cultural collections and applications; european and national projects; metadata, identifiers and semantics in decentralized applications, blockchains and P2P systems