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Register a new userSolving Aspect Category Sentiment Analysis as a Text Generation Task
حل تحليل المعنويات الفئة في آراء كهامة جيل نصي
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اجتذبت تحليل المعنويات الفئة في الآراء اهتمام الأبحاث المتزايد.تستخدم الأساليب المهيمنة نماذج لغة مدربة مسبقا عن طريق تعلم تمثيلات فعالة من الفئة من الفئة، وإضافة طبقات إخراج محددة إلى تمثيلها المدرب مسبقا.نحن نعتبر طريقة أكثر مباشرة لاستخدام نماذج اللغة المدربة مسبقا، من خلال إلقاء مهام ACSA في مهام توليد اللغة الطبيعية، باستخدام جمل اللغة الطبيعية لتمثيل الإخراج.تتيح لطريقتنا استخدام المزيد من الاستخدام المباشر للمعرفة المدربة مسبقا في طرازات اللغة SEQ2SEQ من خلال إعداد المهام مباشرة أثناء التدريب المسبق.تشير التجارب في العديد من المعايير إلى أن طريقتنا تمنح أفضل النتائج المبلغ عنها، حيث توجد مزايا كبيرة في إعدادات قليلة وإعدادات طلقة صفرية.
Aspect category sentiment analysis has attracted increasing research attention. The dominant methods make use of pre-trained language models by learning effective aspect category-specific representations, and adding specific output layers to its pre-trained representation. We consider a more direct way of making use of pre-trained language models, by casting the ACSA tasks into natural language generation tasks, using natural language sentences to represent the output. Our method allows more direct use of pre-trained knowledge in seq2seq language models by directly following the task setting during pre-training. Experiments on several benchmarks show that our method gives the best reported results, having large advantages in few-shot and zero-shot settings.
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Aspect Category Sentiment Analysis (ACSA), which aims to identify fine-grained sentiment polarities of the aspect categories discussed in user reviews. ACSA is challenging and costly when conducting it into real-world applications, that mainly due to
the following reasons: 1.) Labeling the fine-grained ACSA data is often labor-intensive. 2.) The aspect categories will be dynamically updated and adjusted with the development of application scenarios, which means that the data must be relabeled frequently. 3.) Due to the increase of aspect categories, the model must be retrained frequently to fast adapt to the newly added aspect category data. To overcome the above-mentioned problems, we introduce a novel Meta Multi-Task Learning (MMTL) approach, that frame ACSA tasks as a meta-learning problem (i.e., regarding aspect-category sentiment polarity classification problems as the different training tasks for meta-learning) to learn an ideal and shareable initialization for the multi-task learning model that can be adapted to new ACSA tasks efficiently and effectively. Experiment results show that the proposed approach significantly outperforms the strong pre-trained transformer-based baseline model, especially, in the case of less labeled fine-grained training data.
Sentiment analysis has attracted increasing attention in e-commerce. The sentiment polarities underlying user reviews are of great value for business intelligence. Aspect category sentiment analysis (ACSA) and review rating prediction (RP) are two es
sential tasks to detect the fine-to-coarse sentiment polarities. ACSA and RP are highly correlated and usually employed jointly in real-world e-commerce scenarios. While most public datasets are constructed for ACSA and RP separately, which may limit the further exploitation of both tasks. To address the problem and advance related researches, we present a large-scale Chinese restaurant review dataset ASAP including 46, 730 genuine reviews from a leading online-to-offline (O2O) e-commerce platform in China. Besides a 5-star scale rating, each review is manually annotated according to its sentiment polarities towards 18 pre-defined aspect categories. We hope the release of the dataset could shed some light on the field of sentiment analysis. Moreover, we propose an intuitive yet effective joint model for ACSA and RP. Experimental results demonstrate that the joint model outperforms state-of-the-art baselines on both tasks.
In this paper, we investigate the Aspect Category Sentiment Analysis (ACSA) task from a novel perspective by exploring a Beta Distribution guided aspect-aware graph construction based on external knowledge. That is, we are no longer entangled about h
ow to laboriously search the sentiment clues for coarse-grained aspects from the context, but how to preferably find the words highly related to the aspects in the context and determine their importance based on the public knowledge base. In this way, the contextual sentiment clues can be explicitly tracked in ACSA for the aspects in the light of these aspect-related words. To be specific, we first regard each aspect as a pivot to derive aspect-aware words that are highly related to the aspect from external affective commonsense knowledge. Then, we employ Beta Distribution to educe the aspect-aware weight, which reflects the importance to the aspect, for each aspect-aware word. Afterward, the aspect-aware words are served as the substitutes of the coarse-grained aspect to construct graphs for leveraging the aspect-related contextual sentiment dependencies in ACSA. Experiments on 6 benchmark datasets show that our approach significantly outperforms the state-of-the-art baseline methods.
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.
Sentiment analysis aims to detect the overall sentiment, i.e., the polarity of a sentence, paragraph, or text span, without considering the entities mentioned and their aspects. Aspect-based sentiment analysis aims to extract the aspects of the given
target entities and their respective sentiments. Prior works formulate this as a sequence tagging problem or solve this task using a span-based extract-then-classify framework where first all the opinion targets are extracted from the sentence, and then with the help of span representations, the targets are classified as positive, negative, or neutral. The sequence tagging problem suffers from issues like sentiment inconsistency and colossal search space. Whereas, Span-based extract-then-classify framework suffers from issues such as half-word coverage and overlapping spans. To overcome this, we propose a similar span-based extract-then-classify framework with a novel and improved heuristic. Experiments on the three benchmark datasets (Restaurant14, Laptop14, Restaurant15) show our model consistently outperforms the current state-of-the-art. Moreover, we also present a novel supervised movie reviews dataset (Movie20) and a pseudo-labeled movie reviews dataset (moviesLarge) made explicitly for this task and report the results on the novel Movie20 dataset as well.
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