  Collaborative Filtering(CF) recommender is a crucial application in the
online market and ecommerce. However, CF recommender has been proven to suffer
from persistent problems related to sparsity of the user rating that will
further lead to a cold-start issue. Existing methods address the data sparsity
issue by applying token-level sentiment analysis that translate text review
into sentiment scores as a complement of the user rating. In this paper, we
attempt to optimize the sentiment analysis with advanced NLP models including
BERT and RoBERTa, and experiment on whether the CF recommender has been further
enhanced. We build the recommenders on the Amazon US Reviews dataset, and tune
the pretrained BERT and RoBERTa with the traditional fine-tuned paradigm as
well as the new prompt-based learning paradigm. Experimental result shows that
the recommender enhanced with the sentiment ratings predicted by the fine-tuned
RoBERTa has the best performance, and achieved 30.7% overall gain by comparing
MAP, NDCG and precision at K to the baseline recommender. Prompt-based learning
paradigm, although superior to traditional fine-tune paradigm in pure sentiment
analysis, fail to further improve the CF recommender.
