Special Libraries, April 1945

Volume 36, Issue 4https://scholarworks.sjsu.edu/sla_sl_1945/1003/thumbnail.jp

Digital graphic representation in fashion companies

The clothing business demands agility, which is a fundamental factor for the companies to survive in the market. It is known that the micro and small firms undertake all their efforts to survive through each season, seeking to keep its pace in accordance to the latest trends and tendencies. In this process, the technical drawing is applied as a way to express ideas and proposals from the fashion designer towards new products. This work brings the results and its nuances from the research conducted in the clothing segment to verify the situation of this important work tool, and the points that need immediate improvement to reach real gains in productivity

Figurative Messages and Affect in Twitter: Differences Between #irony, #sarcasm and #not

This is the author’s version of a work that was accepted for publication in Knowledge-Based Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Knowledge-Based Systems 108 (2016) 132–143. DOI 10.1016/j.knosys.2016.05.035.The use of irony and sarcasm has been proven to be a pervasive phenomenon in social media posing a challenge to sentiment analysis systems. Such devices, in fact, can influence and twist the polarity of an utterance in different ways. A new dataset of over 10,000 tweets including a high variety of figurative language types, manually annotated with sentiment scores, has been released in the context of the task 11 of SemEval-2015. In this paper, we propose an analysis of the tweets in the dataset to investigate the open research issue of how separated figurative linguistic phenomena irony and sarcasm are, with a special focus on the role of features related to the multi-faceted affective information expressed in such texts. We considered for our analysis tweets tagged with #irony and #sarcasm, and also the tag #not, which has not been studied in depth before. A distribution and correlation analysis over a set of features, including a wide variety of psycholinguistic and emotional features, suggests arguments for the separation between irony and sarcasm. The outcome is a novel set of sentiment, structural and psycholinguistic features evaluated in binary classification experiments. We report about classification experiments carried out on a previously used corpus for #irony vs #sarcasm. We outperform in terms of F-measure the stateof-the-art results on this dataset. Overall, our results confirm the difficulty of the task, but introduce new data-driven arguments for the separation between #irony and #sarcasm. Interestingly, #not emerges as a distinct phenomenon. © 2016 Elsevier B.V. All rights reserved.The National Council for Science and Technology (CONACyT Mexico) has funded the research work of Delia Irazu Hernandez Farias (Grant No. 218109/313683 CVU-369616). Paolo Rosso has been partially funded by SomEMBED MINECO research project (TIN2015-71147-C2-1-P) and by the Generalitat Valenciana under the grant ALMAMATER (PrometeoII/2014/030). The work of Viviana Patti was partially carried out at the Universitat Politecnica de Valencia within the framework of a fellowship of the University of Turin co-funded by Fondazione CRT (WWS Program 2).Sulis, E.; Hernandez-Farias, DI.; Rosso, P.; Patti, V.; Ruffo, G. (2016). Figurative Messages and Affect in Twitter: Differences Between #irony, #sarcasm and #not. Knowledge-Based Systems. 108:132-143. https://doi.org/10.1016/j.knosys.2016.05.035S13214310