Aspect Level Sentiment Analysis

Aspect-level sentiment analysis (ALSA) aims to identify the sentiment expressed towards specific aspects or features within a text, going beyond simple positive/negative classification. Current research focuses on improving accuracy by addressing challenges like noisy data, ambiguous word meanings, and complex sentence structures, employing techniques such as graph neural networks, ensemble methods, and large language models (LLMs) to better capture contextual information and inter-aspect relationships. ALSA's advancements have significant implications for various applications, including business intelligence (e.g., analyzing customer reviews) and AI development (e.g., enhancing decision-making in complex domains like chess strategy evaluation). The field is actively exploring ways to leverage knowledge graphs and improve the handling of coreference and implicit sentiment.