BERT: The Aspect-Based Sentiment Analyser

While BERT is great for sentiment analysis in natural language processing, it has a few limitations too.

- Dipankar Ray

Sentiment analysis of a complex sentence often requires a contextual understanding of the entire sentence rather than understanding the words in isolation or from leftto-right. Such contextual understanding requires simultaneous bi-directional reading of the sentence. Bidirectional Encoder Representations from Transformers, i.e., BERT, is considered the gold standard for Aspect-Based Sentiment Analysis (ABSA) because of its unique ability to understand context and directionality of a sentence and has become non-negotiable for it.

ABSA is an advanced NLP technique that can analyse texts beyond general sentiment (e.g., "This phone is okay") to specific feature-level insights (e.g., "The battery is great (+), but the camera is blurry (-)").

The ABSA concept can be divided into four key terms.

Aspect Terms (AT): The specific words used to describe a feature (e.g., 'attended', 'cost').

Aspect Categories (AC): The categories of the aspect term (e.g., 'Service', 'Hardware').

Opinion Terms (OT): The adjectives used to qualify the aspect term (e.g., 'slow', 'brilliant').

Sentiment polarity: The final classification-Positive, Negative, or Neutral.

Implementation pipeline

Step 1: Aspect Extraction (AE)