Aspect Based Sentiment Classification

Aspect-based sentiment classification (ABSC) aims to identify the sentiment expressed towards specific aspects within a text, going beyond simple positive/negative classification. Current research focuses on improving model accuracy and interpretability using techniques like attention mechanisms, graph-based models, and transformer architectures such as BERT, often incorporating contextual information and addressing challenges like data imbalance and few-shot learning scenarios. ABSC has significant implications for various applications, including understanding customer reviews, analyzing social media sentiment, and even evaluating urban environments based on crowdsourced feedback, providing more nuanced insights than traditional sentiment analysis.