In order to solve a specific case that each sample has two emotional labels at most in emotion classification of Chinese microblog text, a simple multi-label ranking algorithm named TSMLR was proposed. The proposed algorithm employed the strategy of two-stage learning and two-stage classification, and gave classification and ranking emotional labels for each microblog text by learning the relations between labels. Firstly, it transformed the emotion classification problem into eight single-label classification problems. One learning model was trained for the dominant emotion and seven learning models were trained for the secondary emotion. It classified for the dominant emotion label at first, then chose the corresponding classification model for the secondary emotion label. The experiment was conducted on the dataset of Chinese Weibo Texts provided by NLP&CC2014. The results showed that the proposed method improved the accuracy and average precision by 8.59% and 9.28% respectively, and decreased the one-error by 9.77% accordingly, compared to the method of Calibrated Label Ranking (CLR). In addition, the running time of the proposed method was lower than those of the two baseline methods. These experimental results illustrate that the proposed algorithm can effectively learn the label order and make more accurate emotion classification for Chinese microblog.

In user-based collaborative filtering recommendation based on social relations, sometimes the ratings for the target items can not be predicted. Whats more, in traditional item-based collaborative filtering, there are still some items which are not in the same class with the target item and not suitable to be references for predicting ratings. To handle these problems, two new algorithms of collaborative filtering recommendation were proposed, in which the tags of scenic spots type were introduced to compute the similarity between two scenic spots. The experimental results on the data set of scenic spots ratings show that, compared with the user-based collaborative filtering recommendation algorithms based on social relations, the algorithm based on the social relation and tag can increase the accuracy and the coverage by 10% and 4% respectively, and compared with the item-based collaborative filtering recommendation algorithms, the collaborative filtering recommendation algorithm based on item and tag can increase the accuracy by 15%, it also shows that introducing the tags of scenic spots type can make the computation of the similarity between two scenic spots more accurate.