Aspect-Based Sentiment Analysis Using Large Language Models on Museum Visitor Reviews

Abstract

Museum reviews provide rich insight into visitor preferences and can drive useful change within institutions, yet they have attracted little attention in sentiment research owing to limited commercial interest and the multi-thematic nature of reviews. In this study we analysed over 12 000 reviews in Russian for 15 museum sites collected from nine different platforms. Methodologically, we first evaluated traditional approaches: a lexicon-based method utilising sentiment dictionaries and a neural network approach leveraging open-source pre-trained models such as RuBERT. While such methods can be applied to document-level sentiment analysis, where the text is labelled simply as positive or negative, they cannot uncover the specific topics that give rise to these sentiments. Finally, we implemented large language models (LLMs) for aspect-based sentiment analysis to discover positive and negative aspects visitors mention. Our system uses a two-step pipeline that initially extracts positive and negative keywords about each museum site and subsequently categorises these keywords into 14 predetermined categories, enabling the reader to effortlessly discover strong points and areas for improvement. Results include 15 csv tables of positive and negative keywords and 15 year-wise text reports for all objects. While some LLM hallucinations were observed, the outputs were largely realistic. We conclude that LLMs are well suited to this task and offer substantial scope for future research and practical applications in museum evaluation and service improvement.