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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 Hits 1 – 20 of 24 1 Measuring the quality of unstructured text in routinely collected electronic health data: a review and application Nesca, Marcello. - 2022 BASE Show details 2 LEXICON BASED RULE EXTRACTION FOR SENTIMENT ANALYSIS UNDER BIG DATA ENVIRONMENT ... B. Sevugamoorthy. - : Zenodo, 2019 BASE Show details 3 LEXICON BASED RULE EXTRACTION FOR SENTIMENT ANALYSIS UNDER BIG DATA ENVIRONMENT ... B. Sevugamoorthy. - : Zenodo, 2019 BASE Show details 4 NgramPOS: A Bigram-based Linguistic and Statistical Feature Process Model for Unstructured Text Classification Lau, Sian; Yazdania, Sepideh; Tan, Zhiyuan. - : BMC, 2018 BASE Show details 5 Big Data Text Summarization: Using Deep Learning to Summarize Theses and Dissertations Kahu, Sampanna; Ingram, William A.; Jude, Palakh. - : Virginia Tech, 2018 BASE Show details 6 Face value of companies: deep learning for nonverbal communication ... Burgard, Sophie. - : Humboldt-Universität zu Berlin, 2017 BASE Show details 7 Face value of companies: deep learning for nonverbal communication Burgard, Sophie. - : Humboldt-Universität zu Berlin, 2017 BASE Show details 8 Supervised Process of Un-structured Data Analysis for Knowledge Chaining Quantin, Matthieu; Hervy, Benjamin; Laroche, Florent... In: Procedia CIRP ; CIRP design conference ; https://hal.archives-ouvertes.fr/hal-01347030 ; CIRP design conference, KTH, Jun 2016, Stockholm, Sweden. pp.436-441, ⟨10.1016/j.procir.2016.04.123⟩ ; http://cirpdesign2016.org/ (2016) BASE Show details 9 Leveraging Lexical Link Analysis (LLA) To Discover New Knowledge Zhao, Ying; MacKinnon, Douglas J.; Gallup, Shelley P.... In: Military Cyber Affairs (2016) BASE Show details 10 A Corpus Driven Computational Intelligence Framework for Deception Detection in Financial Text Minhas, Saliha Z. - : University of Stirling, 2016 BASE Show details 11 Situation Tracking in Large Data Streams Smith, Andrew; Wiles, Janet In: DTIC (2015) BASE Show details 12 Sentiment Big Data Flow Analysis by Means of Dynamic Linguistic Patterns Hussain, Amir; Bisio, Federica; Gelbukh, Alexander; Poria, Soujanya; Cambria, Erik. - : IEEE, 2015 Abstract: Emulating the human brain is one of the core challenges of computational intelligence, which entails many key problems of artificial intelligence, including understanding human language, reasoning, and emotions. In this work, computational intelligence techniques are combined with common-sense computing and linguistics to analyze sentiment data flows, i.e., to automatically decode how humans express emotions and opinions via natural language. The increasing availability of social data is extremely beneficial for tasks such as branding, product positioning, corporate reputation management, and social media marketing. The elicitation of useful information from this huge amount of unstructured data, however, remains an open challenge. Although such data are easily accessible to humans, they are not suitable for automatic processing: machines are still unable to effectively and dynamically interpret the meaning associated with natural language text in very large, heterogeneous, noisy, and ambiguous environments such as the Web. We present a novel methodology that goes beyond mere word-level analysis of text and enables a more efficient transformation of unstructured social data into structured information, readily interpretable by machines. In particular, we describe a novel paradigm for real-time concept-level sentiment analysis that blends computational intelligence, linguistics, and common-sense computing in order to improve the accuracy of computationally expensive tasks such as polarity detection from big social data. The main novelty of the paper consists in an algorithm that assigns contextual polarity to concepts in text and flows this polarity through the dependency arcs in order to assign a final polarity label to each sentence. Analyzing how sentiment flows from concept to concept through dependency relations allows for a better understanding of the contextual role of each concept in text, to achieve a dynamic polarity inference that outperforms state-of-the- art statistical methods in terms of both accuracy and training time. Keyword: artificial intelligence; Behavioral science; Big Data; big social data; Biological system modeling; common-sense computing; computational intelligence technique; computational linguistics; data flow analysis; data structures; dynamic linguistic pattern; dynamic polarity inference; Electronic circuits; human brain; Internet; Knowledge based systems; Learning systems; Linguistics; natural language text; natural languages; Pragmatics; Semantics; Sentiment analysis; sentiment data flow analysis; social media marketing; social networking (online); statistical methods; text analysis; unstructured data; word-level text analysis URL: https://doi.org/10.1109/MCI.2015.2471215 http://hdl.handle.net/1893/23766 http://dspace.stir.ac.uk/bitstream/1893/23766/1/sentiment-data-flow-analysis.pdf BASE Hide details 13 Lexical Link Analysis Application: Improving Web Service to Acquisition Visibility Portal Zhao, Ying; Gallup, Shelley P; MacKinnon, Douglas J In: DTIC (2013) BASE Show details 14 Automated Extraction and Characterisation of Social Network Data from Unstructured Sources -- An Ontology-Based Approach Martineau, Etienne; Lecocq, Regine In: DTIC (2013) BASE Show details 15 Applications of Lexical Link Analysis Web Service for Large-Scale Automation, Validation, Discovery, Visualization, and Real-Time Program Awareness Zhao, Ying; Gallup, Shelley P; MacKinnon, Douglas J In: DTIC (2012) BASE Show details 16 System Self-Awareness and Related Methods for Improving the Use and Understanding of Data within DoD Zhao, Ying; Mackinnon, Douglas J.; Gallup, Shelley P.. - 2011 BASE Show details 17 Collective knowledge systems: Where the social web meets the semantic web Tom Gruber In: http://www.websemanticsjournal.org/papers/2007119/CollectiveKnowledgeSystemsGruberV6I1.pdf (2008) BASE Show details 18 A conceptual-modeling approach to extracting data from the web D. W. Embley; D. M. Campbell; Y. S. Jiang... In: http://www.deg.byu.edu/papers/er98.pdf (1998) BASE Show details 19 A Conceptual-Modeling Approach to Extracting Data from the Web D.W. Embley; D.M. Campbell; Y.S. Jiang... In: http://osm7.cs.byu.edu/deg/papers/er98.ps (1998) BASE Show details 20 A Conceptual-Modeling Approach to Extracting Data from the Web Embley Campbell; D. W. Embley; D. M. Campbell... In: http://lantern.cs.byu.edu/papers/er98.ps (1998) BASE Show details Page: 1 2

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