Sentiment analysis is one of the traditional well-known tasks in Natural Language
Processing (NLP) research. In recent years, Pre-trained Models (PMs) have become one of …

Although latent factor models (eg, matrix factorization) achieve good accuracy in rating
prediction, they suffer from several problems including cold-start, non-transparency, and …

Personalized rating prediction is an important research problem in recommender systems.
Although the latent factor model (eg, matrix factorization) achieves good accuracy in rating …

Neural network methods have achieved promising results for sentiment classification of text.
However, these models only use semantics of texts, while ignoring users who express the …

Twitter sentiment analysis has become a hot research topic in recent years. Most of existing
solutions to Twitter sentiment analysis basically only consider textual information of Twitter …

We present a neural network method for review rating prediction in this paper. Existing
neural network methods for sentiment prediction typically only capture the semantics of texts …

Document-level sentiment classification is a fundamental problem which aims to predict a
user's overall sentiment about a product in a document. Several methods have been …

In product reviews, it is observed that the distribution of polarity ratings over reviews written
by different users or evaluated based on different products are often skewed in the real …

Topic and sentiment joint modelling has been successfully used in sentiment analysis for
product reviews. However, the problem of text sparse is universal with the widespread smart …

Sentiment classification is a topic-sensitive task, ie, a classifier trained from one topic will
perform worse on another. This is especially a problem for the tweets sentiment analysis …