Reproduce “Beyond the Star Rating: A Scalable Framework for Aspect-Based Sentiment Analysis Using LLMs and Text Classification” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Vishal Patil, Shree Vaishnavi Bacha, Revanth Yamani, Yidan Sun, Mayank Kejriwal
Researchers from the University of Southern California's Information Sciences Institute developed a scalable hybrid framework for Aspect-Based Sentiment Analysis (ABSA) that combines large language models for initial aspect identification with efficient classical machine learning for classifying sentiment across millions of restaurant reviews. This framework demonstrated a strong predictive capability, with an R-squared over 0.81, in explaining overall restaurant ratings based on fine-grained aspect sentiments.
Key insights
- LLMs are highly effective for precise aspect discovery from a sample of data, leveraging their advanced natural language understanding.
- Combining LLM-driven aspect identification with efficient classical classifiers offers a scalable and cost-effective solution for industrial-scale Aspect-Based Sentiment Analysis.
- Automatically extracted aspect-based sentiments provide strong explanatory power for overall customer ratings, offering granular insights that surpass simple aggregate scores.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.