Aspect-based Sentiment Analysis in Question Answering Forums (2021)

Abstract: Aspect-based sentiment analysis (ABSA) typically focuses on extracting aspects and predicting their sentiments on individual sentences such as customer reviews. Recently, another kind of opinion sharing platform, namely question answering (QA) forum, has received increasing popularity, which accumulates a large number of user opinions towards various aspects. This motivates us to investigate the task of ABSA on QA forums (ABSA-QA), aiming to jointly detect the discussed aspects and their sentiment polarities for a given QA pair. Unlike review sentences, a QA pair is composed of two parallel sentences, which requires interaction modeling to align the aspect mentioned in the question and the associated opinion clues in the answer. To this end, we propose a model with a specific design of cross-sentence aspect-opinion interaction modeling to address this task. The proposed method is evaluated on three real-world datasets and the results show that our model outperforms several strong baselines adopted from related state-of-the-art models.

## introduction aspect-based sentiment analysis (absa) usually involves two sub-tasks including aspect term extraction (ate) and aspect sentiment classification (asc) @xcite . for an example sentence "the feel of the restaurant was crowded but the food is great.", ate is to detect the mentioned aspects "feel" and "food", whereas supposing aspects are given, asc predicts their sentiment polarities as negative and positive respectively. given the broad application scenarios, the two sub-tasks @xcite @xcite and their joint q: how about the screen? is this phone's battery life durable? thanks in advance! a: not as large as i thought. but the battery is quite good, i like it. task input output ate-qa qa pair [screen]; [battery life] asc-qa qa pair + [screen] neg qa pair + [battery life] pos absa-qa qa pair [screen] neg [battery life] pos prediction @xcite @xcite have received increasing attention in recent years. most existing absa studies focus on a single opinionated sentence such as the customer review @xcite . besides product reviews, another kind of opinion sharing platform, namely question answering (qa) forum, has been provided on many e-commerce websites, due to the rising demand for users and sellers to communicate with the former buyers to obtain their opinions towards various aspects of the concerned product @xcite . thus, investigating the absa task on such qa forums (denoted as absa-qa) can be a meaningful problem for revealing the rich opinion information from those qa pairs. several attempts have been made on analyzing the sentiment information in qa forums. however, they either predict an overall sentiment polarity towards the entire qa pair @xcite or only consider partial absa-qa problems. for example, @xcite tackle the asc-qa task under the assumption that the targeted aspects are given. as illustrated in figure 1 , they perform aspect-level sentiment classification according to both of the qa pair and the input aspect. however, obtaining the discussed aspects is not a trivial task, which is especially difficult for those qa pairs involving multiple aspects. inspired by previous success on jointly solving the two sub-tasks in review-oriented absa @xcite @xcite , we aim to handle the absa-qa task in a similar unified setting in this work foot_0 . as shown in figure 1 , given a question-answer pair, our goal is to jointly detect the discussed aspect(s) and predict their corresponding sentiment polarities. to tackle the absa-qa task, an intuitive idea would be concatenating the question and answer sentence, then employing the existing absa models to solve it. however, the question and answer sentence are two parallel sequences, therefore, simply concatenating them cannot produce a semanticfluent expression. in such a concatenation, the aspect terms and their corresponding opinion words do not appear next or near to each other, making the position clue utilized by many absa models, i.e., the aspect modifier is closer to the corresponding aspect term in the sentence, invalid @xcite . to make matters worse, it will result in wrong proximity relation, for instance, compared with "quite good", "not as large as" is nearer to "battery life" in the example. meanwhile, because the opinions are expressed in an interactive manner, i.e., the question asks about one or multiple aspects and the answer expresses the opinions towards them, the aspect terms are likely to be omitted or rephrased in the answer sentence. returning to the example in figure 1 , the aspect "battery life" is shortened to "battery" while the explicit mention of the aspect "screen" is directly omitted in the answer. this requires the model to capture the aspect-opinion interactions between the qa pair to align the concerned aspect in the question with the opinions expressed in the answer instead of simply treating them as a consecutive sequence. in this paper, we formulate the absa-qa task as a sequence labeling problem on the question text with the unified tagging scheme denoting both the aspect boundary and sentiment polarity for each word. because of the interactive nature of qa pairs, when predicting the unified tags for the question text, it is essential to utilize the answer information to locate the aspect terms as well as predict their sentiments. to this end, we propose a novel model with cross-sentence aspect-opinion interac-tion modeling to tackle the absa-qa task. specifically, our model is built on top of the pre-trained bert network, which has shown its effectiveness in the general absa problem @xcite @xcite . firstly, to capture the interactions between the question and answer, an inter-qa attention mechanism is employed, which aligns the aspect in the question with the corresponding opinions in the answer. a gated fusion layer is then designed for combining the information from the answer and the question itself to obtain an enriched aspect-aware question representation. next, we employ attentive encoding to summarize the main opinion information from the answer text into the question representation and use two types of cnn layers to refine the final representation and control the sentiment consistency. finally, the refined feature representation for each question token is fed to a linear layer to predict the unified tag. in addition to the base model described above, we exploit two auxiliary tasks to further enhance it: (i) an auxiliary aspect term extraction task is introduced to better guide the learning of the aspect-aware question representation. (ii) to improve the interaction modeling across the sentence pair, we propose to pre-train the related components with qa pair matching task for obtaining the prior knowledge on aligning two sentences. in summary, our main contributions are as follows: (1) we study the absa-qa task, aiming to jointly detect the discussed aspects and their sentiment polarities for a given qa pair. (2) we propose a model that carefully captures the cross-sentence aspect-opinion interactions and utilize two auxiliary tasks for better feature representation learning to tackle the concerned task. (3) we conduct extensive experiments on real-world datasets across three domains and the results show that our proposed model outperforms several strong baselines adopted from related state-of-the-art models. ## methodology we formulate the absa-qa task as a sequence labeling problem on the question text and employ a unified tagging scheme: y @xmath0, i, e, s}-{pos, neu, neg} ∪ {o} to jointly denote the aspect term and its sentiment polarity for each token following @xcite . the former part of the tag defines the boundary of the aspect whereas the latter refers to its sentiment polarity. given a qa pair including a question @xmath1, q 2 , . . . , q m } and its answer @xmath2, a 2 , . . . , a n }, we aim to detect the discussed aspects and their sentiment polarities by predicting a tag sequence @xmath3, y 2 , . . . , y m } for the question text where y @xmath4. ## related work aspect-based sentiment analysis (absa) has been extensively studied in recent years @xcite . it is often decomposed into two sub-tasks. the first aspect term extraction (ate) task aims to detect the mentioned aspect @xcite @xcite @xcite . the second aspect sentiment classification (asc) task then predicts the sentiment polarity, assuming an aspect is given @xcite @xcite . since separately handling these two tasks ignores the relations between them and leads to unsatisfactory performance, recent works attempt to solve it in a unified framework. these studies either adopt a unified tagging scheme @xcite or solving them in a multi-task learning paradigm with shared feature representations @xcite . recently, there are also some attempts of combining another related task, namely opinion term extraction (ote), with the ate and/or asc tasks to provide a more complete understanding of the aspect-level user sentiment @xcite @xcite @xcite . however, most existing studies target at customer reviews @xcite or twitter posts @xcite . thus the proposed methods are often tailored for observations made in single-sentence situation. for example, many models consider the position clues between the aspect term and the opinion terms since they often appear next or near to each other in the reviews @xcite . given the rising popularity of question answering (qa) forums @xcite , some studies aim at extracting sentiment information on them. @xcite treat the qa pair as a whole and predict its sentiment polarity. @xcite further consider the syntax information in qa to improve the prediction. however, these work ignore the aspect-level information and only predict "conflict" if there are multiple aspects involved. @xcite focus on the asc task in the qa forums (asc-qa) which assumes the aspect is already given for the classification. unlike these existing work, we investigate the unified absa-qa task in this paper, aiming to jointly tackle the ate-qa and asc-qa problem. ## conclusions in this paper, we investigate the aspect-based sentiment analysis in question answering forums (absa-qa), aiming to jointly detect the discussed aspects and their sentiment polarities for a given qa pair. we demonstrate the challenges of conducting absa in qa settings and propose a model with carefully designed cross-sentence aspect-opinion interaction to tackle the task. moreover, we utilize two auxiliary tasks including aspect term extraction task for learning better aspectaware representation and qa pair matching task to pre-train the inter-qa attention components to for better aligning the question and answer sentence. extensive experiments are conducted on three real-world datasets, showing the superiority of our proposed model against various baselines.