The present disclosure relates to the technical field of
artificial intelligence, and particularly relates to a product review aspect
sentiment analysis method and device,
electronic equipment, storage medium and program product. The product review aspect
sentiment analysis method comprises: obtaining target product review data; inputting the target product review data and a prompt instruction into a fine-tuning model to obtain an aspect category and a sentiment tendency corresponding to the target product review data; wherein the fine-tuning model is obtained by performing LoRA fine-tuning on a base model based on sample product review data and a preset prompt instruction Prompt template. The present disclosure uses a fine-tuning model obtained by fine-tuning a base model in a LoRA lightweight manner, and realizes product review fine-grained
sentiment analysis in combination with a prompt instruction, without relying on a static sentiment dictionary and a
syntax analyzer, can effectively adapt to network new words, dynamic
semantics and non-standard comments in
spoken language, and can accurately identify multi-dimensional product aspects and corresponding sentiment tendencies in complex comments.