AX - MABSA : A Framework for Extremely Weakly Supervised Multi-label Aspect Based Sentiment Analysis (2022)

Abstract: Aspect Based Sentiment Analysis is a dominant research area with potential applications in social media analytics, business, finance, and health. Prior works in this area are primarily based on supervised methods, with a few techniques using weak supervision limited to predicting a single aspect category per review sentence. In this paper, we present an extremely weakly supervised multi-label Aspect Category Sentiment Analysis framework which does not use any labelled data. We only rely on a single word per class as an initial indicative information. We further propose an automatic word selection technique to choose these seed categories and sentiment words. We explore unsupervised language model post-training to improve the overall performance, and propose a multi-label generator model to generate multiple aspect category-sentiment pairs per review sentence. Experiments conducted on four benchmark datasets showcase our method to outperform other weakly supervised baselines by a significant margin.

## introduction aspect-based sentiment analysis (absa) is a wellknown sentiment analysis task which provides more fine-grained information than simple sentiment understanding @xcite . the main goal of absa is to find the aspects and its associated sentiment within a given text. while the works on absa have expanded in different directions, it has primarily two sub-tasks, aspect term sentiment analysis (atsa) and aspect category sentiment analysis (acsa) @xcite . atsa consists of different tasks like aspect term extraction @xcite @xcite , aspect term sentiment classification @xcite @xcite , opinion term extraction (dai and song, 1 code, data and model are available at https://github. com/sabyasachi-kamila/ax-mabsa 2019; @xcite , aspect-oriented opinion term extraction @xcite , aspect-opinion pair extraction @xcite ), etc. for example, in the sentence "the sushi is top-notch, the waiter is attentive, but the atmosphere is dull.", atsa would extract the aspect terms 'sushi', 'waiter' and 'atmosphere'; opinion terms 'top-notch', 'attentive', and 'dull'; and their associated sentiments 'positive', 'positive' and 'negative'. the other sub-task acsa aims to find the higher order aspect categories and its associated sentiment from a given text. in the above example, acsa would detect the categories as 'food' (as 'pasta' is a type of 'food'), 'service' and 'ambience'; and the associated sentiments as 'positive', 'positive' and 'negative'. existing research on absa is dominated by supervised methods, where labeled training data is provided @xcite @xcite @xcite . a few works try to solve the problem in a weakly/semi-supervised manner, where a few labelled samples are provided @xcite . however, there has been a lack of study on absa using unsupervised methods, i.e., without using any labelled data. a few works also focused on unsupervised aspect term extraction @xcite . however, such works do not deal with the sentiment associated with the aspects. an existing work on weakly supervised acsa @xcite only considered a single aspect category per sentence -thus limiting the task to a larger extent. motivated by the above, in this work, we present a methodology for extremely weakly supervised acsa task, where we do not need any labelled training samples. we solve both aspect category detection (acd) and acsa tasks (on each review sentence) just by using the surface text of aspect category and sentiment. given n review sentences, c categories of interest and p polarities of interest, the acd task generates c clusters, while the acsa task generates (c i , p j ) tuples where c @xmath0, and p @xmath1. as in @xcite , we adopt the representation learning perspective, wherein representing sentences by class names leads to better clustering. we only use the surface text of the class names and unlabelled sentences to get aspect category and sentiment clusters. however, in clustering, each review sentence would get only one label, thus limiting the task by a substantial extent. to tackle this, we propose x-mabsa, a multi-label generator model which makes use of dependency parser @xcite and a similarity-based attention mechanism to generate multiple categories and associated sentiment polarity labels for each review sentence. in addition, we find that sometimes the representative text of aspect categories (provided as input) is not present (or sparse) in the text corpus. this might lead to skewed representation of the classes in our framework and thus degrade performance. therefore, we present an automatic surface word selection strategy which would represent the class names better. we combine this with our x-mabsa model and denote it as ax-mabsa. we also showcase that unsupervised posttraining of language model on domain specific data significantly improves the sentence representation and thus achieves better results for acsa tasks. for this, we post-train bert language model @xcite using domain specific unlabelled data. we perform experiments on four different benchmark aspect-based datasets @xcite @xcite , and compare with different supervised and weakly supervised baselines. our main contributions are as follows: ## related work aspect based sentiment analysis (absa) has gained significant attention for a long time, and research has been done in primarily two directions -aspect term sentiment analysis (atsa) and aspect category sentiment analysis (acsa). ## proposed methodology our proposed method, ax-mabsa, works on the following components: (a) class name-based clustering, (b) unsupervised language model posttraining on domain-specific data for better contextual representation of review sentences, (c) a multi-label generator model to generate multiple categories and associated sentiment labels, and (d) automatic class-representative text selection. the overall framework is depicted in figure 1 . ## experimental setup we discuss here the datasets we have used, word representations, and different baselines we have selected for our experiments. foot_1 ## experimental evaluation in this section, we study the performance of the different algorithms on four datasets, compare them with different baselines, and discuss the qualitative analysis of our model performance. ## conclusion in this paper, we studied extremely weakly supervised aspect category sentiment analysis across four benchmark datasets, and presented the stateof-the-art unsupervised framework without the requirement of any labelled data. our method relied only on the surface text of aspect class names and unlabelled texts to extract aspect-sentiment pairs via a multi-label generator model. we proposed an automatic class-representative surface word selection algorithm to select proper representative words corresponding to each class. we also found that unsupervised post-training of language models on domain-specific data improved the wordrepresentations and thus improved the performance. experiments show that our proposed method performs better than all weakly supervised baseline models. in the future, we intend to improve our methods to incorporate more sentiment classes. we believe that our work would foster more research interest towards unsupervised absa.