6,355 research outputs found
Text Analytics for Android Project
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
Text Analytics: the convergence of Big Data and Artificial Intelligence
The analysis of the text content in emails, blogs,
tweets, forums and other forms of textual communication
constitutes what we call text analytics. Text analytics is applicable
to most industries: it can help analyze millions of emails; you can
analyze customers’ comments and questions in forums; you can
perform sentiment analysis using text analytics by measuring
positive or negative perceptions of a company, brand, or product.
Text Analytics has also been called text mining, and is a subcategory
of the Natural Language Processing (NLP) field, which is one of the
founding branches of Artificial Intelligence, back in the 1950s, when
an interest in understanding text originally developed. Currently
Text Analytics is often considered as the next step in Big Data
analysis. Text Analytics has a number of subdivisions: Information
Extraction, Named Entity Recognition, Semantic Web annotated
domain’s representation, and many more. Several techniques are
currently used and some of them have gained a lot of attention,
such as Machine Learning, to show a semisupervised enhancement
of systems, but they also present a number of limitations which
make them not always the only or the best choice. We conclude
with current and near future applications of Text Analytics
Editorial for the First Workshop on Mining Scientific Papers: Computational Linguistics and Bibliometrics
The workshop "Mining Scientific Papers: Computational Linguistics and
Bibliometrics" (CLBib 2015), co-located with the 15th International Society of
Scientometrics and Informetrics Conference (ISSI 2015), brought together
researchers in Bibliometrics and Computational Linguistics in order to study
the ways Bibliometrics can benefit from large-scale text analytics and sense
mining of scientific papers, thus exploring the interdisciplinarity of
Bibliometrics and Natural Language Processing (NLP). The goals of the workshop
were to answer questions like: How can we enhance author network analysis and
Bibliometrics using data obtained by text analytics? What insights can NLP
provide on the structure of scientific writing, on citation networks, and on
in-text citation analysis? This workshop is the first step to foster the
reflection on the interdisciplinarity and the benefits that the two disciplines
Bibliometrics and Natural Language Processing can drive from it.Comment: 4 pages, Workshop on Mining Scientific Papers: Computational
Linguistics and Bibliometrics at ISSI 201
TextRWeb: Large-Scale Text Analytics with R on the Web
As digital data sources grow in number and size, they pose an opportunity for computational investigation by means of text mining, NLP, and other text analysis techniques. R is a popular and powerful text analytics tool; however, it needs to run in parallel and re- quires special handling to protect copyrighted content against full access (consumption). The HathiTrust Research Center (HTRC) currently has 11 million volumes (books) where 7 million volumes are copyrighted. In this paper we propose HTRC TextRWeb, an interactive R software environment which employs complexity hiding interfaces and automatic code generation to allow large-scale text analytics in a non-consumptive means. For our principal test case of copyrighted data in HathiTrust Digital Library, TextRWeb permits us to code, edit, and submit text analytics methods empowered by a family of interactive web user interfaces. All these methods combine to reveal a new interactive paradigm for large-scale text analytics on the web
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