Attention is an important mechanism that can be employed for a variety of deep learning models across many different domains and tasks. This survey provides an overview of the most important attention mechanisms proposed in the literature. The various attention mechanisms are explained by means of a framework consisting of a general attention model, uniform notation, and a comprehensive taxonomy of attention mechanisms. Furthermore, the various measures for evaluating attention models are reviewed, and methods to characterize the structure of attention models based on the proposed framework are discussed. Last, future work in the field of attention models is considered.

The field of sentiment analysis, in which sentiment is gathered, analyzed, and aggregated from text, has seen a lot of attention in the last few years. The corresponding growth of the field has resulted in the emergence of various subareas, each addressing a different level of analysis or research question. This survey focuses on aspect-level sentiment analysis, where the goal is to find and aggregate sentiment on entities mentioned within documents or aspects of them. An in-depth overview of the current state-of-the-art is given, showing the tremendous progress that has already been made in finding both the target, which can be an entity as such, or some aspect of it, and the corresponding sentiment. Aspect-level sentiment analysis yields very fine-grained sentiment information which can be useful for applications in various domains. Current solutions are categorized based on whether they provide a method for aspect detection, sentiment analysis, or both. Furthermore, a breakdown based on the type of algorithm used is provided. For each discussed study, the reported performance is included. To facilitate the quantitative evaluation of the various proposed methods, a call is made for the standardization of the evaluation methodology that includes the use of shared data sets. Semanticallyrich concept-centric aspect-level sentiment analysis is discussed and identified as one of the most promising future research direction.

With the constantly growing number of reviews and other sentiment-bearing texts on the Web, the demand for automatic sentiment analysis algorithms continues to expand. Aspect-based sentiment classification (ABSC) allows for the automatic extraction of highly fine-grained sentiment information from text documents or sentences. In this survey, the rapidly evolving state of the research on ABSC is reviewed. A novel taxonomy is proposed that categorizes the ABSC models into three major categories: knowledge-based, machine learning, and hybrid models. This taxonomy is accompanied with summarizing overviews of the reported model performances, and both technical and intuitive explanations of the various ABSC models. State-of-the-art ABSC models are discussed, such as models based on the transformer model, and hybrid deep learning models that incorporate knowledge bases. Additionally, various techniques for representing the model inputs and evaluating the model outputs are reviewed. Furthermore, trends in the research on ABSC are identified and a discussion is provided on the ways in which the field of ABSC can be advanced in the future.

As people increasingly use emoticons in text in order to ex- press, stress, or disambiguate their sentiment, it is crucial for automated sentiment analysis tools to correctly account for such graphical cues for sentiment. We analyze how emoti-cons typically convey sentiment and demonstrate how we can exploit this by using a novel, manually created emoticon sentiment lexicon in order to improve a state-of-the-art lexicon-based sentiment classication method. We evaluate our approach on 2,080 Dutch tweets and forum mes- sages, which all contain emoticons and have been manually annotated for sentiment. On this corpus, paragraph-level accounting for sentiment implied by emoticons signicantly improves sentiment classication accuracy. This indicates that whenever emoticons are used, their associated senti- ment dominates the sentiment conveyed by textual cues and forms a good proxy for intended sentiment

Using online consumer reviews as electronic word of mouth to assist purchase-decision making has become increasingly popular. The Web provides an extensive source of consumer reviews, but one can hardly read all reviews to obtain a fair evaluation of a product or service. A text processing framework that can summarize reviews, would therefore be desirable. A subtask to be performed by such a framework would be to find the general aspect categories addressed in review sentences, for which this paper presents two methods. In contrast to most existing approaches, the first method presented is an unsupervised method that applies association rule mining on co-occurrence frequency data obtained from a corpus to find these aspect categories. While not on par with state-of-the-art supervised methods, the proposed unsupervised method performs better than several simple baselines, a similar but supervised method, and a supervised baseline, with an F ₁ -score of 67%. The second method is a supervised variant that outperforms existing methods with an F ₁ -score of 84%.