An Iterative Multi-aspect term Extraction and Polarity Detection Approach Based on spanBERT for Aspect-based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is a fine-grained analysis of sentiments and opinions expressed in text document which set aside the naïve assumption that a document expresses opinion about a single topic or aspect. The main objective of ABSA is to spot, extract and identify the polarity of different entities and aspects in an opinionated document. Based on the previous works, ABSA can be categorized into three subtasks: Aspect-category sentiment analysis (ACSA), Opinion Target Expression Sentiment Analysis (OTESA) and Aspect-term sentiment analysis (ATSA). This research presents an end-to-end multi-task approach to performing the three categories of ABSA on a single pipeline. A ternary multitask learning objectives classifiers were built on top of the baseline spanBERT language model which was originally pretrained for span extraction. The input to the model consists of two merged segments of entity premises and context data hypothesis in a similar passion to reading comprehension downstream task in natural language processing. The ternary downstream tasks were built on the contextualized output embeddings of pretrained spanBERT entangled with cross-layer attention mechanism to associate context with the aspect-term span extraction, aspect sentiment polarity detection and entity-aspect entailment. A span masking approach was also proposed to address multiple-aspects text using an iterative outputs-inputs loopback. The span masking process replaces each word in a previously detected span of text with a special [MASK] character and then feeds back the entire sentence into the encoder input of the model for next run. The technique forces the encoder to look elsewhere for the next span prediction. The loopback span masking terminates when the span classifiers predict a special token [CLS] as the beginning and end of the span signaling the absent of relatable span to be extracted. Experimental results validate the approach as impressive results were obtained outperforming most of the compared research with benchmark ABSA datasets.