Sentiment analysis on user-generated content has achieved notable progress by introducing user information to consider each individual’s preference and language usage. However, most existing approaches ignore the data sparsity problem, where the content of some users is limited and the model fails to capture discriminative features of users. To address this issue, we hypothesize that users could be grouped together based on their rating biases as well as degree of rating consistency and the knowledge learned from groups could be employed to analyze the users with limited data. Therefore, in this paper, a neural group-wise sentiment analysis model with data sparsity awareness is proposed. The user-centred document representations are generated by incorporating a group-based user encoder. Furthermore, a multi-task learning framework is employed to jointly modelusers’ rating biases and their degree of rating consistency. One task is vanilla populationlevel sentiment analysis and the other is groupwise sentiment analysis. Experimental results on three real-world datasets show that the proposed approach outperforms some state-of the-art methods. Moreover, model analysis and case study demonstrate its effectiveness of modeling user rating biases and variances.