Kaklauskas, Arturas, Seniut, Mark, Amaratunga, Dilanthi, Lill, Irene, Safonov, Andrej, Vatin, Nikolai, Cerkauskas, Justas, Jackute, Ieva, Kuzminske, Agne and Peciure, Lina (2014) Text Analytics for Android Project. Procedia Economics and Finance, 18. pp. 610-617. ISSN 2212-5671

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.

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