Text Analytics for Android Project

Arturas Kaklauskas, Mark Seniut, Dilanthi Amaratunga, Irene Lill, Andrej Safonov, Nikolai Vatin, Justas Cerkauskas, Ieva Jackute, Agne Kuzminske, Lina Peciure

Research output: Contribution to journalArticle

Abstract

Most advanced text analytics and text mining tasks include text classification, text clustering, building ontology, concept/entity extraction, summarization, deriving patterns within the structured data, production of granular taxonomies, sentiment and emotion analysis, document summarization, entity relation modelling, interpretation of the output. Already existing text analytics and text mining cannot develop text material alternatives (perform a multivariant design), perform multiple criteria analysis, automatically select the most effective variant according to different aspects (citation index of papers (Scopus, ScienceDirect, Google Scholar) and authors (Scopus, ScienceDirect, Google Scholar), Top 25 papers, impact factor of journals, supporting phrases, document name and contents, density of keywords), calculate utility degree and market value. However, the Text Analytics for Android Project can perform the aforementioned functions. To the best of the knowledge herein, these functions have not been previously implemented; thus this is the first attempt to do so. The Text Analytics for Android Project is briefly described in this article.
Original languageEnglish
Pages (from-to)610-617
Number of pages8
JournalProcedia Economics and Finance
Volume18
Early online date30 Dec 2014
DOIs
Publication statusPublished - 2014
Event4th International Conference on Building Resilience - Salford Quays, Manchester, United Kingdom
Duration: 8 Sep 201411 Sep 2014
Conference number: 4

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    Kaklauskas, A., Seniut, M., Amaratunga, D., Lill, I., Safonov, A., Vatin, N., ... Peciure, L. (2014). Text Analytics for Android Project. Procedia Economics and Finance, 18, 610-617. https://doi.org/10.1016/S2212-5671(14)00982-4