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Sentiment analysis has increasingly gained significance in commercial settings, driven by the rising impact of reviews on purchase decision-making in recent years. This research conducts a thorough examination of the suitability of machine learning and deep learning approaches for sentiment analysis, using Romanian reviews as a case study, with the aim of gaining insights into their practical utility. A comprehensive, multi-level analysis is performed, covering the document, sentence, and aspect levels. The main contributions of the paper refer to the in-depth exploration of multiple sentiment analysis models at three different textual levels and the subsequent improvements brought with respect to these standard models. Additionally, a balanced dataset of Romanian reviews from twelve product categories is introduced. The results indicate that, at the document level, supervised deep learning techniques yield the best outcomes (specifically, a convolutional neural network model that obtains an AUC value of 0.93 for binary classification and a weighted average F1-score of 0.77 in a multi-class setting with 5 target classes), albeit with increased resource consumption. Favorable results are achieved at the sentence level, as well, despite the heightened complexity of sentiment identification. In this case, the best-performing model is logistic regression, for which a weighted average F1-score of 0.77 is obtained in a multi-class polarity classification task with three classes. Finally, at the aspect level, promising outcomes are observed in both aspect term extraction and aspect category detection tasks, in the form of coherent and easily interpretable word clusters, encouraging further exploration in the context of aspect-based sentiment analysis for the Romanian language. [ABSTRACT FROM AUTHOR]

Authorship attribution is the task of determining the likely author of a given text, with applications in domains such as literature and literary history, social network analysis, software engineering and cybersecurity. AutoAt , a deep autoencoder-based classification model which exploits the ability of autoencoders to encode meaningful data patterns is proposed to solve this task. Experiments are conducted on a data set of 1571 poems authored by 8 Romanian poets using a distributed document representation. The proposed approach obtains comparable or better results with respect to other machine learning classifiers. Additionally, the formulation of the AutoAt model allows for the computation of the probability that a test instance belongs to a given author class, which may be a useful property in a variety of authorship attribution applications. This aspect and the fact that AutoAt performs well in the difficult task of authorship attribution on poetic data without the step of feature engineering being informed by domain knowledge show that the proposed classifier is a general one, with potential to be used successfully in other fields. [ABSTRACT FROM AUTHOR]