Analysing and evaluating the task of automatic tweet generation: Knowledge to business

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Title: Analysing and evaluating the task of automatic tweet generation: Knowledge to business
Authors: Lloret, Elena | Palomar, Manuel
Research Group/s: Procesamiento del Lenguaje y Sistemas de Información (GPLSI)
Center, Department or Service: Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos
Keywords: Natural language processing | Text summarisation | Natural language tweet generation | User study | Linguistic analysis | Descriptive statistics
Knowledge Area: Lenguajes y Sistemas Informáticos
Issue Date: May-2016
Publisher: Elsevier
Citation: Computers in Industry. 2016, 78: 3-15. doi:10.1016/j.compind.2015.10.010
Abstract: In this paper a study concerning the evaluation and analysis of natural language tweets is presented. Based on our experience in text summarisation, we carry out a deep analysis on user's perception through the evaluation of tweets manual and automatically generated from news. Specifically, we consider two key issues of a tweet: its informativeness and its interestingness. Therefore, we analyse: (1) do users equally perceive manual and automatic tweets?; (2) what linguistic features a good tweet may have to be interesting, as well as informative? The main challenge of this proposal is the analysis of tweets to help companies in their positioning and reputation on the Web. Our results show that: (1) automatically informative and interesting natural language tweets can be generated as a result of summarisation approaches; and (2) we can characterise good and bad tweets based on specific linguistic features not present in other types of tweets.
Sponsor: This research work has been partially funded by the University of Alicante, Generalitat Valenciana, Spanish Government and the European Commission through the projects, “Tratamiento inteligente de la información para la ayuda a la toma de decisiones” (GRE12-44), “Explotación y tratamiento de la información disponible en Internet para la anotación y generación de textos adaptados al usuario” (GRE13-15), DIIM2.0 (PROMETEOII/2014/001), ATTOS (TIN2012-38536-C03-03), LEGOLANG-UAGE (TIN2012-31224), and SAM (FP7-611312).
ISSN: 0166-3615 (Print) | 1872-6194 (Online)
DOI: 10.1016/j.compind.2015.10.010
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2015 Elsevier B.V.
Peer Review: si
Publisher version:
Appears in Collections:Research funded by the EU
INV - GPLSI - Artículos de Revistas

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