Multi-Instance Multi-Label Learning Networks for Aspect-Category Sentiment Analysis (2020)

Abstract: Aspect-category sentiment analysis (ACSA) aims to predict sentiment polarities of sentences with respect to given aspect categories. To detect the sentiment toward a particular aspect category in a sentence, most previous methods first generate an aspect category-specific sentence representation for the aspect category, then predict the sentiment polarity based on the representation. These methods ignore the fact that the sentiment of an aspect category mentioned in a sentence is an aggregation of the sentiments of the words indicating the aspect category in the sentence, which leads to suboptimal performance. In this paper, we propose a Multi-Instance Multi-Label Learning Network for Aspect-Category sentiment analysis (AC-MIMLLN), which treats sentences as bags, words as instances, and the words indicating an aspect category as the key instances of the aspect category. Given a sentence and the aspect categories mentioned in the sentence, AC-MIMLLN first predicts the sentiments of the instances, then finds the key instances for the aspect categories, finally obtains the sentiments of the sentence toward the aspect categories by aggregating the key instance sentiments. Experimental results on three public datasets demonstrate the effectiveness of AC-MIMLLN.

## introduction sentiment analysis @xcite has attracted increasing attention recently. aspectbased sentiment analysis (absa) @xcite @xcite is a fine-grained sentiment analysis task and includes many subtasks, two of which are aspect category detection (acd) that detects the aspect categories mentioned in a sentence and while it was large and a bit noisy, the drinks were fantastic, and the food was superb. since a sentence usually contains one or more aspect categories, previous studies have developed various methods for generating aspect categoryspecific sentence representations to detect the sentiment toward a particular aspect category in a sentence. to name a few, attention-based models @xcite @xcite allocate the appropriate sentiment words for the given aspect category. @xcite proposed to generate aspect categoryspecific representations based on convolutional neural networks and gating mechanisms. since aspectrelated information may already be discarded and aspect-irrelevant information may be retained in an aspect independent encoder, some existing methods @xcite utilized the given aspect to guide the sentence encoding from scratch. recently, bert based models @xcite have obtained promising performance on the acsa task. however, these models ignored that the sentiment of an aspect category mentioned in a sentence is an aggregation of the sentiments of the words indicating the aspect category. it leads to suboptimal performance of these models. for the example in figure 1 , both "drinks" and "food" indicate the aspect category food. the sentiment about food is a combination of the sentiments of "drinks" and "food". note that, words indicating aspect categories not only contain aspect terms explicitly indicating an aspect category but also contain other words implicitly indicating an aspect category @xcite . in figure 1 , while "drinks" and "food" are aspect terms explicitly indicating the aspect category food, "large" and "noisy" are not aspect terms implicitly indicating the aspect category ambience. in this paper, we propose a multi-instance multilabel learning network for aspect-category sentiment analysis (ac-mimlln). ac-mimlln explicitly models the fact that the sentiment of an aspect category mentioned in a sentence is an aggregation of the sentiments of the words indicating the aspect category. specifically, ac-mimlln treats sentences as bags, words as instances, and the words indicating an aspect category as the key instances @xcite of the aspect category. given a bag and the aspect categories mentioned in the bag, ac-mimlln first predicts the instance sentiments, then finds the key instances for the aspect categories, finally aggregates the sentiments of the key instances to get the bag-level sentiments of the aspect categories. our main contributions can be summarized as follows: multi-instance multi-label learning (mimll) @xcite deals with problems where a training example is described by multiple instances and associated with multiple class labels. mimll has achieved success in various applications due to its advantages on learning with complicated objects, such as image classification @xcite ), text categorization @xcite , relation extraction @xcite , etc. in acsa, a sentence contains multiple words (instances) and expresses sentiments to multiple aspect categories (labels), so mimll is suitable for acsa. however, as far as our knowledge, mimll has not been explored in acsa. multiple instance learning (mil) @xcite ) is a special case of mimll, where a real-world object described by a number of instances is associated with only one class label. some studies @xcite @xcite have applied mil to sentiment analysis. @xcite proposed a multiple instance learning network (milnet), where the overarching polarity of a text is an aggregation of sentence or elementary discourse unit polarities, weighted by their importance. an attention-based polarity scoring method is used to obtain the importance of segments. similar to milnet, our model also uses an attention mechanism to obtain the importance of instances. however, the attention in our model is learned from the acd task, while the attention in milnet is learned from the sentiment classification task. pappas and popescu-belis (2014) applied mil to another subtask of absa. they proposed a multiple instance regression (mir) model to assign sentiment scores to specific aspects of products. however, i) their task is different from ours, and ii) their model is not a neural network. ## model in this section, we describe how to apply the multiinstance multi-label learning framework to the aspect-category sentiment analysis task. we first introduce the problem formulation, then describe our proposed multi-instance multi-label learning network for aspect-category sentiment analysis (ac-mimlln). ## conclusion in this paper, we propose a multi-instance multi-label learning network for aspect-category sentiment analysis (ac-mimlln). ac-mimlln predicts the sentiment of an aspect category mentioned in a sentence by aggregating the sentiments of the words indicating the aspect category in the sentence. experimental results demonstrate the effectiveness of ac-mimlln. since ac-mimlln finds the key instances for the given aspect category and predicts the sentiments of the key instances, it is more interpretable. in some sentences, phrases or clauses rather than words indicate the given aspect category, future work could consider multi-grained instances, including words, phrases and clauses. since directly finding the key instances for some aspect categories is ineffective, we will try to first recognize all opinion snippets in a sentence, then assign these snippets to the aspect categories mentioned in the sentence.