In this paper

we design a topic-features lattices analysis (TFLA) framework based on objectivity quality dimensions. Firstly

we apply the latent Dirichlet allocation (LDA) approach to get latent topics as topic-features for each goods categories. Secondly

we construct formal background based on the strong relationship between goods categories and topic-features. So we could get generalization and instantiation relationship among the topic-features through formal concept analysis (FCA). We employ domain knowledge and relationships among topic-features to define five objective quality features. Also

we use machine learning methods to build quality evaluation models based on these quality features. Experiment results on actual comment data sets show that our new quality models' prediction results are in agreement with the artificial quality tags in most cases. The best performances could get that the mean absolute error (MAE) is 0.7 and F-measure is 0.5

which is significantly better than the conventional quality prediction model based on support vector machine (SVM) classification.