ABSTRACT
Microblogs generate a vast amount of data in which users express their emotions regarding almost all aspects of everyday life. Capturing affective content from these context-dependent and subjective texts is a challenging task. We propose an intelligent probabilistic model for textual emotion recognition in multidimensional space (TERMS) that captures the subjective emotional boundaries and contextual information embedded in a text for robust emotion recognition. It is implausible with discrete label assignment;therefore, the model employs a soft assignment by mapping varying emotional perceptions in a multidimensional space and generates them as distributions via the Gaussian mixture model (GMM). To strengthen emotion distributions, TERMS integrates a probabilistic emotion classifier that captures the contextual and linguistic information from texts. The integration of these aspects, the context-aware emotion classifier and the learned GMM parameters provide a complete coverage for accurate emotion recognition. The large-scale experimentation shows that compared to baseline and state-of-the-art models, TERMS achieved better performance in terms of distinguishability, prediction, and classification performance. In addition, TERMS provide insights on emotion classes, the annotation patterns, and the models application in different scenarios.
ABSTRACT
The permanent transition to online activity has brought with it a surge in hate speech discourse. This has prompted increased calls for automatic detection methods, most of which currently rely on a dictionary of hate speech words, and supervised classification. This approach often falls short when dealing with newer words and phrases produced by online extremist communities. These code words are used with the aim of evading automatic detection by systems. Code words are frequently used and have benign meanings in regular discourse, for instance, "skypes, googles, bing, yahoos" are all examples of words that have a hidden hate speech meaning. Such overlap presents a challenge to the traditional keyword approach of collecting data that is specific to hate speech. In this work, we first introduced a word embedding model that learns the hidden hate speech meaning of words. With this insight on code words, we developed a classifier that leverages linguistic patterns to reduce the impact of individual words. The proposed method was evaluated across three different datasets to test its generalizability. The empirical results show that the linguistic patterns approach outperforms the baselines and enables further analysis on hate speech expressions.
Subject(s)
Hate , Speech , Language , Learning , LinguisticsABSTRACT
The aim of the present study was to develop a simple and fast screening technique to directly evaluate the bactericidal effects of 5-aminolevulinic acid (ALA)-mediated photodynamic inactivation (PDI) and to determine the optimal antibacterial conditions of ALA concentrations and the total dosage of light in vitro. The effects of PDI on Staphylococcus aureus and Pseudomonas aeruginosa in the presence of various concentrations of ALA (1.0 mM, 2.5 mM, 5.0 mM, 10.0 mM) were examined. All bacterial strains were exponentially grown in the culture medium at room temperature in the dark for 60 minutes and subsequently irradiated with 630 ± 5 nm using a light-emitting diode (LED) red light device for accumulating the light doses up to 216 J/cm2. Both bacterial species were susceptible to the ALA-induced PDI. Photosensitization using 1.0 mM ALA with 162 J/cm2 light dose was able to completely reduce the viable counts of S. aureus. A significant decrease in the bacterial viabilities was observed for P. aeruginosa, where 5.0 mM ALA was photosensitized by accumulating the light dose of 162 J/cm2. We demonstrated that the use of microplate-based assays-by measuring the apparent optical density of bacterial colonies at 595 nm-was able to provide a simple and reliable approach for quickly choosing the parameters of ALA-mediated PDI in the cell suspensions.
