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A Novel Neural Network Architecture for Sentiment-oriented Aspect-Opinion Pair Extraction

Series: M.Tech (Research)Thesis Defence -ONLINE

Speaker: Mr. Kapil Jayesh Pathak M.Tech (Research) student Dept. of CSA

Date/Time: Jul 08 10:00:00

Location: Microsoft Teams - ON-LINE

Faculty Advisor: Prof. Shirish K Shevade

Over the years, fine-grained opinion mining in online reviews has received great attention from the NLP research community. It involves different tasks such as Aspect Term Extraction (ATE), Opinion Term Extraction (OTE), etc. Opinion Term Extraction (OTE) aims to detect different opinion expressions which convey certain attitude in the review while Aspect Term Extraction (ATE) aims to identify the entities or proposition from the review at which the attitude is directed. Recently, the NLP research community got attracted to aspect-opinion relation modeling. Such modeling would be helpful for aspect-opinion pair extraction that would be used for downstream tasks such as aspect-based sentiment analysis, opinion summarization, etc. As online reviews may contain different sentiment polarities for different aspects of the products, it would help companies find all aspects for which the customers gave positive or negative feedback. In this thesis, we propose a new opinion mining task called Sentiment-oriented Aspect-Opinion Pair Extraction (SAOPE), which aims to find all aspect-opinion pairs from customer reviews given that these pairs convey the specified sentiment polarity. We present a novel neural network architecture for the SAOPE task. In the proposed approach, aspect-opinion co-extraction is performed first and then the aspect-opinion pairs are generated through relation modeling. The aspect and the corresponding opinion words are closely related in the dependency trees. Hence, we explore graph neural networks to utilize syntactic information generated from the dependency tree of the reviews to model the relationship between the aspects and corresponding opinion words. We design a modified graph attention network (GAT) called Graph Co-attention Network (GCAT) and compare its performance with Graph Convolution Network (GCN) and Graph Attention Network (GAT) for the aspect-opinion co-extraction and the relation detection. For the SAOPE task, we evaluate our model on SemEval Challenge datasets and show that GCAT and GAT perform better than the baseline model with GCN for aspect-opinion co-extraction. We demonstrate that the proposed Graph Co-attention Network (GCAT) performs better than other graph neural networks for aspect-opinion relation detection on the publicly available benchmark datasets. Microsoft teams link:

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