Polytechnic University of Valencia

About: Polytechnic University of Valencia is a education organization based out in Valencia, Spain. It is known for research contribution in the topics: Catalysis & Population. The organization has 16282 authors who have published 40162 publications receiving 850234 citations.

Do clusters capabilities matter? An empirical application of the resource-based view in clusters

Abstract: The resource-based view (RBV) of the firm has been applied to territories, although academia has not frequently undertaken exploration of RBV applied to clusters in an empirical base. The goal of this paper aims at empirically translating RBV to the territory with a double objective. First, the work seeks to understand which are the cluster's resources and capabilities. Second, the paper evaluates whether a cluster's unique set of resources and capabilities could influence a cluster's performance. Research is applied to leading European ceramic tile clusters located in Spain (Castellon) and Italy (Emilia-Romagna). Comparing clusters in the same industry allows benchmarking and the metrics make more sense. Secondary data and face-to-face semi-structured interviews with managers from the R&D Institutes, institutional agents and Castellon (59) and Emilian (19) firms assess a cluster's resources and capabilities. The employed variables address skilled labour availability, social capital, linkages, business so...

Figurative messages and affect in Twitter

Abstract: 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 state-of-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.