TY - JOUR
T1 - Text Analytics for Android Project
AU - Kaklauskas, Arturas
AU - Seniut, Mark
AU - Amaratunga, Dilanthi
AU - Lill, Irene
AU - Safonov, Andrej
AU - Vatin, Nikolai
AU - Cerkauskas, Justas
AU - Jackute, Ieva
AU - Kuzminske, Agne
AU - Peciure, Lina
N1 - Conference code: 4
PY - 2014/12/30
Y1 - 2014/12/30
N2 - 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.
AB - 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.
KW - text analytics
KW - text mining
KW - Text Analytics for Android Project
KW - qualitative and quantitative analysis
U2 - 10.1016/S2212-5671(14)00982-4
DO - 10.1016/S2212-5671(14)00982-4
M3 - Conference article
VL - 18
SP - 610
EP - 617
JO - Procedia Economics and Finance
JF - Procedia Economics and Finance
SN - 2212-5671
T2 - 4th International Conference on Building Resilience
Y2 - 8 September 2014 through 11 September 2014
ER -