1.《urban sensing using smartphones for transportation mode classification》
（1）A key finding of our work is that walking activity can be robustly detected and acts as a separator for partitioning the data into single mode.
（2）Our approach yields high accuracy despite the low sampling interval and does not require GPS data。
（3）Device power consumption is effectively minimized。
2.《Transportaion Mode Annotation of Tourist GPS trajectories Under environmental constraints》
文章思想根据现今城市的设施（如train只适合在轨道上行驶）来进行推断。创新点：A new, simple GPS semantic annotation method using environmental constraints without machine learning. It is necessary for machine learning to label the data for training, the requirement is costly.
3.《Comparison of advanced imputation algorithms for detection of transportation mode and activity episode using GPS data》
1.《Influence Factor Based Opinion Mining of Twitter Data Using Supervised Learning》
内容：This paper proposes a hybrid approach of extracting opinion using direct and indirect features of Twitter data based on Support Vector Machines (SVM), Naive Bayes,Maximum Entropy and Artificial Neural Networks based supervised classifiers.
2.《Opinion Mining and Sentiment Analysis on a Twitter Data Stream》
内容：This paper discusses an approach where a publicised stream of tweets from the Twitter microblogging site are preprocessed and classified based on their emotional content as positive, negative and irrelevant; and analyses the performance of various classifying algorithms based on their precision and recall in such cases.
3.《A Semi-supervised Fuzzy Co-clustering Framework and Application to Twitter Data Analysis》
内容：In this paper, a novel framework for performing fuzzy co-clustering of cooccurrence information with partial supervision is proposed, which is induced by multinomial mixture concept.