A bert-based algorithm for fusion sentiment analysis of user comments

This study proposes a recommendation system model that combines BERT, weighted fusion attention mechanism, and multi-aspect feature matching (BERT_Aspect_Attn_Fusion, BAAF). The model aims to enhance recommendation accuracy for Digital Music and Yelp review datasets by integrating user and product comments. The diversity and complexity of the sentiment expressed in these datasets require the model to process a large amount of text information and potential user preferences.The BERT model is used to process the user and product review texts, capturing the deep semantic information within the comments. By encoding each review into a context-aware vector, the model fully leverages the pre-trained language model to extract the underlying features of the comments.The weighted fusion attention mechanism calculates attention scores for each review, automatically identifying the important reviews related to user or product preferences. This mechanism assigns different weights to each review based on its semantic significance, which in turn influences the final recommendation outcome.Multi-aspect matching involves matching the user and product comment representations across multiple dimensions, further exploring the potential relationships within the reviews, such as emotional commonality and the interaction between product and user.Factorization Machine (FM) is used to explicitly model the interactions between features, especially second-order interactions, which effectively improve the model's prediction capabilities when handling sparse data and high-dimensional features.This model not only provides a more accurate understanding of the complex relationships between users and products but also improves the performance of recommendation systems in domains like Digital Music and Yelp reviews. By merging semantic information, emotional expression, and user preferences, the model can offer more personalized and precise recommendations.