Aspect and Opinion Term Extraction Using Graph Attention Network

Extracting information from customer feedback is a key capability required for identifying current drawbacks and scope for further improvement. Online shoppers routinely provide feedback on their experience with the purchased product that are not just important for the other potential customers but also a critical feedback to the product manufacturers for the next cycle of iteration. Similar feedbacks are available in various other domains ranging from Manufacturing to Healthcare where granular opinions (sentiments) about various dimensions (aspects) of the used product (or service) are available in textual form but need to be understood. Due to the presence of multiple aspects (and the corresponding sentiments) extraction of these aspect-sentiment pairs is a challenging task and since its introduction in 2014 (SemEval-2014 Task-4, Pontiki et al.) Aspect Based Sentiment Analysis (ABSA) attracted various different approaches and is still under active consideration. ABSA demands semantic understanding of the sentence where it is necessary to identify the aspect terms (defining "what") and the opinion terms (defining "why") and the connection between each related pairs (resulting in a positive, neutral or negative sentiment, i.e., "how").