Search (2 results, page 1 of 1)

  • × author_ss:"Tian, J."
  1. He, R.; Wang, J.; Tian, J.; Chu, C.-T.; Mauney, B.; Perisic, I.: Session analysis of people search within a professional social network (2013) 0.00 
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    Abstract
    We perform session analysis for our domain of people search within a professional social network. We find that the content-based method is appropriate to serve as a basis for the session identification in our domain. However, there remain some problems reported in previous research which degrade the identification performance (such as accuracy) of the content-based method. Therefore, in this article, we propose two important refinements to address these problems. We describe the underlying rationale of our refinements and then empirically show that the content-based method equipped with our refinements is able to achieve an excellent identification performance in our domain (such as 99.820% accuracy and 99.707% F-measure in our experiments). Next, because the time-based method has extremely low computation costs, which makes it suitable for many real-world applications, we investigate the feasibility of the time-based method in our domain by evaluating its identification performance based on our refined content-based method. Our experiments demonstrate that the performance of the time-based method is potentially acceptable to many real applications in our domain. Finally, we analyze several features of the identified sessions in our domain and compare them with the corresponding ones in general web search. The results illustrate the profession-oriented characteristics of our domain.
    Date
    19. 4.2013 20:31:22
  2. Chen, Z.; Huang, Y.; Tian, J.; Liu, X.; Fu, K.; Huang, T.: Joint model for subsentence-level sentiment analysis with Markov logic (2015) 0.00 
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    Abstract
    Sentiment analysis mainly focuses on the study of one's opinions that express positive or negative sentiments. With the explosive growth of web documents, sentiment analysis is becoming a hot topic in both academic research and system design. Fine-grained sentiment analysis is traditionally solved as a 2-step strategy, which results in cascade errors. Although joint models, such as joint sentiment/topic and maximum entropy (MaxEnt)/latent Dirichlet allocation, are proposed to tackle this problem of sentiment analysis, they focus on the joint learning of both aspects and sentiments. Thus, they are not appropriate to solve the cascade errors for sentiment analysis at the sentence or subsentence level. In this article, we present a novel jointly fine-grained sentiment analysis framework at the subsentence level with Markov logic. First, we divide the task into 2 separate stages (subjectivity classification and polarity classification). Then, the 2 separate stages are processed, respectively, with different feature sets, which are implemented by local formulas in Markov logic. Finally, global formulas in Markov logic are adopted to realize the interactions of the 2 separate stages. The joint inference of subjectivity and polarity helps prevent cascade errors. Experiments on a Chinese sentiment data set manifest that our joint model brings significant improvements.

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