Aspect terms extraction (ATE), a key subtask for aspect-based sentiment analysis, opinion summarization, and topic modeling aims at extracting grammatical elements (nouns, phrases, and adjectives) from user reviews that reveal the discussed features of the entity under review. These aspect terms are usually the targets of the opinions expressed. Identifying them requires tackling substantial linguistic challenges but, due to the multiple commercial and social applications, significant research effort has been invested in efficiently mining aspects. Recent advances in ATE address methods that exploit a sentence or a word-level encoding of a user review as a solution. This article proposes a novel and effective word- and sentence-level encoding framework, which utilizes a neural network architecture that learns to extract aspect terms. The main advantage of our approach is that it can extract explicit and implicit aspects (i.e., aspects that are not directly mentioned in the user-generated text). We evaluate our method on four widely used datasets where we prove its efficiency against state-of-the-art alternative approaches.

As forecasting becomes more and more appreciated in situations and activities of everyday life that involve prediction and risk assessment, more methods and solutions make their appearance in this exciting arena of uncertainty. However, less is known about what makes a promising or a poor forecast. In this article, we provide a multi-factor analysis on the forecasting methods that participated and stood out in the M4 competition, by focusing on Error (predictive performance), Correlation (among different methods), and Complexity (computational performance). The main goal of this study is to recognize the key elements of the contemporary forecasting methods, reveal what made them excel in the M4 competition, and eventually provide insights towards better understanding the forecasting task.