SDRS: a new lossless dimensionality reduction for text corpora

In recent years, most content-based spam filters have been implemented using Machine Learning (ML) approaches by means of token-based representations of textual contents. After introducing multiple performance enhancements, the impact has been virtually irrelevant. Recent studies have introduced synset-based content representations as a reliable way to improve classification, as well as different forms to take advantage of semantic information to address problems, such as dimensionality reduction. These preliminary solutions present some limitations and enforce simplifications that must be gradually redefined in order to obtain significant improvements in spam content filtering. This study addresses the problem of feature reduction by introducing a new semantic-based proposal (SDRS) that avoids losing knowledge (lossless). Synset-features can be semantically grouped by taking advantage of taxonomic relations (mainly hypernyms) provided by BabelNet ontological dictionary (e.g. “Viagra” and “Cialis” can be summarized into the single features “anti-impotence drug”, “drug” or “chemical substance” depending on the generalization of 1, 2 or 3 levels). In order to decide how many levels should be used to generalize each synset of a dataset, our proposal takes advantage of Multi-Objective Evolutionary Algorithms (MOEA) and particularly, of the Non-dominated Sorting Genetic Algorithm (NSGA-II). We have compared the performance achieved by a Naïve Bayes classifier, using both token-based and synset-based dataset representations, with and without executing dimensional reductions. As a result, our lossless semantic reduction strategy was able to find optimal semantic-based feature grouping strategies for the input texts, leading to a better performance of Naïve Bayes classifiers.info:eu-repo/semantics/acceptedVersio

Multi-objective evolutionary optimization for dimensionality reduction of texts represented by synsets

Despite new developments in machine learning classification techniques, improving the accuracy of spam filtering is a difficult task due to linguistic phenomena that limit its effectiveness. In particular, we highlight polysemy, synonymy, the usage of hypernyms/hyponyms, and the presence of irrelevant/confusing words. These problems should be solved at the pre-processing stage to avoid using inconsistent information in the building of classification models. Previous studies have suggested that the use of synset-based representation strategies could be successfully used to solve synonymy and polysemy problems. Complementarily, it is possible to take advantage of hyponymy/hypernymy-based to implement dimensionality reduction strategies. These strategies could unify textual terms to model the intentions of the document without losing any information (e.g., bringing together the synsets “viagra”, “ciallis”, “levitra” and other representing similar drugs by using “virility drug” which is a hyponym for all of them). These feature reduction schemes are known as lossless strategies as the information is not removed but only generalised. However, in some types of text classification problems (such as spam filtering) it may not be worthwhile to keep all the information and let dimensionality reduction algorithms discard information that may be irrelevant or confusing. In this work, we are introducing the feature reduction as a multi-objective optimisation problem to be solved using a Multi-Objective Evolutionary Algorithm (MOEA). Our algorithm allows, with minor modifications, to implement lossless (using only semantic-based synset grouping), low-loss (discarding irrelevant information and using semantic-based synset grouping) or lossy (discarding only irrelevant information) strategies. The contribution of this study is two-fold: (i) to introduce different dimensionality reduction methods (lossless, low-loss and lossy) as an optimization problem that can be solved using MOEA and (ii) to provide an experimental comparison of lossless and low-loss schemes for text representation. The results obtained support the usefulness of the low-loss method to improve the efficiency of classifiers.info:eu-repo/semantics/publishedVersio

Case studies of electrical characterisation of graphene by terahertz time-domain spectroscopy

Graphene metrology needs to keep up with the fast pace of developments in graphene growth and transfer. Terahertz time-domain spectroscopy (THz-TDS) is a non-contact, fast, and non-destructive characterization technique for mapping the electrical properties of graphene. Here we show several case studies of graphene characterization on a range of different substrates that highlight the versatility of THz-TDS measurements and its relevance for process optimization in graphene production scenarios

Facebook affordances and citizen engagement during elections: European political parties and their benefit from online strategies?

This paper examines how Facebook is used by political parties during elections to extend or accelerate their reach within the electorate and how successful these efforts are. Specifically, we compare the content and style of parties’ Facebook posts during the 2014 European parliament elections, and how this affects followers’ responses in terms of liking, sharing and commenting on the posts. Our findings reveal while that the timing and visual content of posts are important in increasing voters’ attention, interactivity matters most. Responsive party posts on Facebooks are significantly more likely to be shared, liked, and commented on by users. Given that follower reactions, particularly sharing, helps to increase the visibility of party communication through indirect or two-step flow communication (online and offline), these findings are important in advancing our understanding of how and why social media campaigns are able to influence voters and thus affect election outcomes. For parties themselves the results provide some useful insights into what makes for an ‘effective’ Facebook campaign in terms of how they can accelerate the reach of their communication