Multi-label feature selection has been widely used in many domains, such as image classification and disease diagnosis. However, there usually exist missing labels in the label space of data in practice, which destroys the structure and correlation between labels, so that the learning algorithms are difficult to exactly select important features. To address this problem, a Multi-label Feature Selection based on Label-specific feature with Missing Labels (MFSLML) algorithm was proposed. Firstly, the label-specific feature for each class label was obtained via sparse learning method. At the same time, the mapping relations between labels and label-specific features were constructed based on linear regression model, and were used to recover the missing labels. Finally, experiments were performed on 7 datasets with using 4 evaluation metrics. Experimental results show that compared to some state-of-the-art multi-label feature selection algorithms, such as multi-label feature selection algorithm based Max-Dependency and Min-Redundancy (MDMR) and the Multi-label Feature selection with Missing Labels via considering feature interaction (MFML), MFSLML can increase the average precision by 4.61-5.5 percentage points. It can be seen that MFSLML achieves better classification performance.

Aiming at the problem that the traditional Laplacian score for feature selection cannot be directly applied to multi-label tasks, a multi-label feature selection algorithm based on Laplacian score was proposed. Firstly, the sample similarity matrix was reconstructed by the correlation of the common and non-correlated correlations of the samples in the overall label space. Then, the correlation and redundancy between features were introduced into Laplacian score, and a forward greedy search strategy was designed to evaluate the co-operation ability between candidate features and selected features, which was used to evaluate the importance of candidate features. Finally, extensive experiments were conducted on six multi-label data sets with five different evaluation criteria. The experimental results show that compared with Multi-label Dimensionality reduction via Dependence Maximization (MDDM), Feature selection for Multi-Label Naive Bayes classification (MLNB) and feature selection for multi-label classification using multivariate mutual information (PMU), the proposed algorithm not only has the best classification performance, but also has a remarkable performance of up to 65%.

The analysis of sentiment-aspect for product or service is useful for finding the information of sentiment-aspect from the mess of comment set. This paper proposed a new method of sentiment-aspect based on seed words of aspect. Firstly, seed words of aspect and documents of aspect automatically could be achieved by this method. Secondly, Sentiment-Aspect Analysis model Supervised by Seed Words (SAA_SSW) was employed by this method to find aspect and related sentiment. The experimental results show that, compared with traditional Joint Sentiment/Topic Model (JST) and Aspect and Sentiment Unification Model (ASUM), SAA_SSW can find the sentiment labels for same word under different topics and achieve higher relevance between sentiment word and topic. In addition, SAA_SSW model, compared with traditional JST and ASUM model, can improve the classification accuracy by at least 7.5%. So, SAA_SSW model can achieve the extraction of sentiment-aspect well and improve the classification accuracy.

Author Topic (AT) model is widely used to find the author's interests in scientific literature, but AT model cannot take advantage of the correlation between category labels and topics. Through integrating the inherent category labels of documents into AT model, Labeled Author Topic (LAT) model was proposed. LAT model realized the predicate of multi-labels by optimizing the mapping relation between labels and topics and improved the clustering results. The experimental results suggest that, compared with Latent Dirichlet Allocation (LDA) model and AT model, LAT model can improve the decision accuracy of multi-labels, and optimize the generalization ability and operating efficiency.