Data Augmentation in a Hybrid Approach for Aspect-Based Sentiment Analysis

Data augmentation is a way to increase the diversity of available data by applying constrained transformations on the original data. This strategy has been widely used in image classification but has to the best of our knowledge not yet been used in aspect-based sentiment analysis (ABSA). ABSA is a text analysis technique that determines aspects and their associated sentiment in opinionated text. In this paper, we investigate the effect of data augmentation on a state-of-the-art hybrid approach for aspect-based sentiment analysis (HAABSA). We apply modified versions of easy data augmentation (EDA), backtranslation, and word mixup. We evaluate the proposed techniques on the SemEval 2015 and SemEval 2016 datasets. The best result is obtained with the adjusted version of EDA, which yields a 0.5 percentage point improvement on the SemEval 2016 dataset and 1 percentage point increase on the SemEval 2015 dataset compared to the original HAABSA model.Comment: The 36th ACM/SIGAPP Symposium On Applied Computing, Virtual Conference, March 22-March 26, 20

Evaluation of the Atellica® UAS 800: a new member of the automated urine sediment analyzer family

BACKGROUND: In 2017 the Atellica® UAS 800 urine sediment analyzer was introduced by Siemens Healthineers. We investigated its applicability in the standardization and automation of the laboratory urinalysis workflow, including the prediction of urine culture outcome and glomerular pathology. METHODS: We evaluated the performance characteristics of the Atellica® UAS 800 and its correlation with the iQ200 (Beckman Coulter). In addition, we studied the agreement between Atellica® UAS 800 and CLINITEK Novus® and determined the predictive value of bacteria and leukocyte counts for urine culture outcome. Furthermore, we investigated the ability of Atellica® UAS 800 to identify pathological casts and dysmorphic erythrocytes in comparison to manual microscopy. RESULTS: Erythrocyte and leukocyte analyses indicated high intra- and inter-run precisions and good correlations with the iQ200. We found that the Atellica® UAS 800 detects bacteria with higher sensitivity than the iQ200. The Atellica® UAS 800 and CLINITEK Novus® showed a high degree of conformity. We determined seven combinations of clinical cut-off values of bacteria and leukocytes for predicting urine culture outcome with sensitivity, specificity, and negative predictive values of 95%, 52%, and 93%, respectively. Using the Atellica® UAS 800, hyaline casts, erythrocyte casts, leukocyte casts, and dysmorphic erythrocytes were correctly recognized in 76%, 22%, 2%, and 39% of the samples, respectively. CONCLUSIONS: The Atellica® UAS 800 is a robust, fast, and user-friendly analyzer, which accurately quantifies erythrocytes, leukocytes, bacteria and squamous epithelial cells, and may be utilized for predicting positive urine cultures. The detection of clinically important pathological casts and dysmorphic erythrocytes proved insufficient