Aspect Sentiment Classification with Aspect-Specific Opinion Spans (2020)

Abstract: Aspect based sentiment analysis, predicting sentiment polarity of given aspects, has drawn extensive attention. Previous attention-based models emphasize using aspect semantics to help extract opinion features for classification. However, these works are either not able to capture opinion spans as a whole, or not able to capture variable-length opinion spans. In this paper, we present a neat and effective structured attention model by aggregating multiple linear-chain CRFs. Such a design allows the model to extract aspect-specific opinion spans and then evaluate sentiment polarity by exploiting the extracted opinion features. The experimental results on four datasets demonstrate the effectiveness of the proposed model, and our analysis demonstrates that our model can capture aspect-specific opinion spans.

## introduction aspect based sentiment analysis (absa) @xcite is an extensively studied sentiment analysis task on a fine-grained semantic level, i.e., opinion targets explicitly mentioned in sentences. previous absa studies focused on a few sub-tasks, such as aspect sentiment classification (asc) @xcite @xcite , aspect term extraction (ate) @xcite , aspect and opinion co-extraction @xcite @xcite , e2e-absa (a joint task of asc and ate) @xcite @xcite , aspect sentiment triplet extraction (aste) @xcite , etc. asc analyzes the sentiment polarity of given aspects/targets in a review. for example, consider the review sentence "food is usually very good, though occasionally i worry about freshness of raw vegetables in side orders." this review mentions two aspects: food and raw vegetables, and for asc, the objective is to give a positive sentiment on food and a negative sentiment on raw vegetables. most of the previous works @xcite @xcite @xcite @xcite adopt attention mechanism @xcite to capture the semantic relatedness among the context words and the aspect, and learn aspect-specific features for sentiment classification. however, it is challenging for attention-based approaches to consider an opinion span as a whole during feature extraction because they are overreliant on neural models to learn the contextstructural information and perform feature extraction over individual hidden representations. previous work @xcite engage structured attention networks @xcite , which extend the previous attention mechanism to incorporate structure dependencies, to model the interaction among context words, and perform softselections of word spans. in particular, they introduce two hand-coded regularizers to constrain the soft-selection process to attend to few short opinion spans. however, such regularizers disturb the structure dependencies, and their method is not capable of emphasizing aspect-specific opinion spans for sentiment classification. to better capture opinion features for aspect sentiment classification, we propose the mcrf-sa model, which introduces multiple conditional random fields (crf) @xcite to structured attention model. while exploiting the advantages of structured attention mechanisms, our model avoids the regularizers by the complementarity among multiple crfs. we also improve the previous position decay function @xcite to reduce the importance of context words that are further away from the aspect so as to emphasize aspect-specific opinion spans. our multi-crf layer with the effective decay function extracts aspect-specific features from different representation sub-spaces to overcome the previous limitations. the experimental results on the four datasets demonstrate the effectiveness of our model, and the analysis shows that the behaviors are in alignment with our intuition. ## model description given a context sequence w @xmath0, w 2 , . . . , w n } and a aspect sequence w @xmath1, ..., w j } (1 ≤ @xmath2, the goal of asc is to predict sentiment polarity @xmath3, negative, neutral} over the given aspect. our model is mainly constructed with a few neural layers, including an input layer, an aspect-specific contextualized representation layer, a position decay layer, a multi-crf structured attention layer, and a sentiment classification layer. figure 1 presents the architecture of our mcrf-sa model. ## conclusion we propose a simple and effective mcrf-sa model to extract aspect-specific opinion span features. in particular, with the proposed multi-crf structured attention layer and the effective position decay function, our model is capable of extracting various aspect-specific opinion span features from different representation sub-spaces. the experimental results demonstrate that our method effectively exploits the corresponding opinion features for sentiment classification. one future direction is to investigate how to integrate the two different attention mechanisms, namely the standard attention and structured attention for nlp applications.