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    Classifying Social Media Users with Machine Learning
    Gang Li,Huayang Zhou,Jin Mao,Sijing Chen
    2019, 3 (8): 1-9.  DOI: 10.11925/infotech.2096-3467.2018.1207
    Abstract   HTML ( 39 PDF(1064KB) ( 320 )  

    [Objective] This paper uses multi-dimensional information of social media users to automatically classify them. [Methods] First, we defined social media users as individual, media, government, and organization. Then, we extracted the following features from user profiles: demographic characteristics, namings, and self-descriptions. Third, we created a user classification models based on machine learning algorithms and evaluated its performance with real Twitter dataset. [Results] Both precision and recall of the proposed model were greater than 83%. The naming, demographic characteristics, and self-description features posed increasing contributions to the classification model. [Limitations] The sample size needs to be expanded, which helps us better analyzed the characteristics of different users. [Conclusions] The proposed method could accurately identify four types of users, which benefits social media user classification research in the future.

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    Sentiment Analysis for Online User Reviews Based on Tripartite Network
    Weicong Lu,Jian Xu
    2019, 3 (8): 10-20.  DOI: 10.11925/infotech.2096-3467.2018.1030
    Abstract   HTML ( 20 PDF(5343KB) ( 149 )  

    [Objective] The paper proposes a tripartite network sentiment analysis method, aiming to reflect the indirect connections between nodes. [Methods] We constructed a “user-product-sentiment tag” tripartite network, which were split into three bipartite networks for network structure analysis. Then, we used the proposed tripartite network projection method to obtain the “two-sentiment one-mode” network of users and products. [Results] We obtained the association of high-weighted related nodes from NetEase Cloud music dataset, and information such as genre classifications, hot-rated songs, and fan groups. [Limitations] The large number of user nodes need to be visualized in the future. [Conclusions] Based on the formation, splitting and projection of the sentiment tripartite network, we present the indirect connection between nodes, and provide new perspectives for network sentiment analysis.

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    Measuring Tech-Entropy of System Evolution: An Empirical Study of Patents
    Jianhua Hou,Pan Liu
    2019, 3 (8): 21-29.  DOI: 10.11925/infotech.2096-3467.2018.0904
    Abstract   HTML ( 6 PDF(635KB) ( 107 )  

    [Objective] This paper measures the developments and the life cycles of the technology system with an improved technology entropy method, aiming to provide theoretical foundation for predicting technology development and decision-making of the governments. [Methods] We constructed a model measuring technological entropy based on information entropy and multiple indicators for the patented technology system. Then, we conducted an empirical analysis with the new model for carbon capture technology in China. [Results] We found that the target technology concluded the stages of sprouting, and slow growth. It is currently in the stage of rapid growth. [Limitations] The quality of the sample data needs to be improved. [Conclusions] The proposed method is an effective way to analyze the evolution trends of patent technology system, which provides a better solution for identifying the life cycle of technologies.

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    POI Recommendation Based on Geographic and Social Relationship Preferences
    Yan Wen,Lijian Ma,Qingtian Zeng,Wenyan Guo
    2019, 3 (8): 30-39.  DOI: 10.11925/infotech.2096-3467.2018.0764
    Abstract   HTML ( 9 PDF(1295KB) ( 121 )  

    [Objective] This study tries to improve the POI recommendation based on user’s geographic information and social relationships. [Methods] First, we proposed a MFDR model (MF with Distance-entropy and Refined-social-regularization), which introduced the concept of distance-entropy to refine user’s preferences and the frequency-based user-interest-matrix. Then, we applied the user-relationship-interest-matrix to refine the preferences with their social-relationship. Finally, we used the regularization-based matrix factorization method to factorize the user-preference-matrix and user-relationship-interest-matrix to ensure their consistency. [Results] We examined the new model with Gowalla and Brightkite check-in datasets, and found it outperformed existing POI recommendation algorithms. When the number of latent factors was 10 and the number of recommended POI was 10, the precision and recall of MFDR on Gowalla reached 4.47% and 9.95%. These results were 30.71% and 28.93% higher than those of traditional POI recommendation models. [Limitations] The expeimental datasets need to be expanded. [Conclusions] The proposed MFDR model based on geographical preference refinement and social-relationship preference implicit analysis is an effective way to recommend POI.

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    Evaluating Information Services of Online Health Q&A Platform
    Chuang Hong,He Li,Lihui Peng,Yiming Xu
    2019, 3 (8): 41-52.  DOI: 10.11925/infotech.2096-3467.2018.1482
    Abstract   HTML ( 11 PDF(585KB) ( 100 )  

    [Objective] This paper explores the evaluation methods for information services of online health Q&A platform, aiming to promote its sustainable development. [Methods] We introduced the SERVQUAL framework and established assessment indicators and extension evaluation model. [Results] We examined the proposed model with Dingxiang Doctor, a health Q&A platform in China, to evaluat