Search (10 results, page 1 of 1)

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Abstract

The method of latent semantic indexing (LSI) is well-known for tackling the synonymy and polysemy problems in information retrieval; however, its performance can be very different for various datasets, and the questions of what characteristics of a dataset and why these characteristics contribute to this difference have not been fully understood. In this article, we propose that the mathematical structure of simplexes can be attached to a term-document matrix in the vector space model (VSM) for information retrieval. The Q-analysis devised by R.H. Atkin ([1974]) may then be applied to effect an analysis of the topological structure of the simplexes and their corresponding dataset. Experimental results of this analysis reveal that there is a correlation between the effectiveness of LSI and the topological structure of the dataset. By using the information obtained from the topological analysis, we develop a new method to explore the semantic information in a dataset. Experimental results show that our method can enhance the performance of VSM for datasets over which LSI is not effective.

Source

Journal of the American Society for Information Science and Technology. 61(2010) no.3, S.592-608

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Abstract

This work identifies changes in dominant topics in library and information science (LIS) over time, by analyzing the 3,121 doctoral dissertations completed between 1930 and 2009 at North American Library and Information Science programs. The authors utilize latent Dirichlet allocation (LDA) to identify latent topics diachronically and to identify representative dissertations of those topics. The findings indicate that the main topics in LIS have changed substantially from those in the initial period (1930-1969) to the present (2000-2009). However, some themes occurred in multiple periods, representing core areas of the field: library history occurred in the first two periods; citation analysis in the second and third periods; and information-seeking behavior in the fourth and last period. Two topics occurred in three of the five periods: information retrieval and information use. One of the notable changes in the topics was the diminishing use of the word library (and related terms). This has implications for the provision of doctoral education in LIS. This work is compared to other earlier analyses and provides validation for the use of LDA in topic analysis of a discipline.

Source

Journal of the American Society for Information Science and Technology. 62(2011) no.1, S.185-204

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Abstract

With the information overload of user-generated content in microblogging, users find it extremely challenging to browse and find valuable information in their first attempt. In this paper we propose a microblogging recommendation algorithm, TSI-MR (Topic-Level Social Influence-based Microblogging Recommendation), which can significantly improve users' microblogging experiences. The main innovation of this proposed algorithm is that we consider social influences and their indirect structural relationships, which are largely based on social status theory, from the topic level. The primary advantage of this approach is that it can build an accurate description of latent relationships between two users with weak connections, which can improve the performance of the model; furthermore, it can solve sparsity problems of training data to a certain extent. The realization of the model is mainly based on Factor Graph. We also applied a distributed strategy to further improve the efficiency of the model. Finally, we use data from Tencent Weibo, one of the most popular microblogging services in China, to evaluate our methods. The results show that incorporating social influence can improve microblogging performance considerably, and outperform the baseline methods.

Source

Journal of the Association for Information Science and Technology. 68(2017) no.3, S.553-568

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Abstract

Image Ranking is one of the key problems in information science research area. However, most current methods focus on increasing the performance, leaving the semantic gap problem, which refers to the learned ranking models are hard to be understood, remaining intact. Therefore, in this article, we aim at learning an interpretable ranking model to tackle the semantic gap in fine-grained image ranking. We propose to combine attribute-based representation and online passive-aggressive (PA) learning based ranking models to achieve this goal. Besides, considering the highly localized instances in fine-grained image ranking, we introduce a supervised constrained clustering method to gather class-balanced training instances for local PA-based models, and incorporate the learned local models into a unified probabilistic framework. Extensive experiments on the benchmark demonstrate that the proposed framework outperforms state-of-the-art methods in terms of accuracy and speed.

Source

Journal of the Association for Information Science and Technology. 69(2018) no.12, S.1488-1501

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Abstract

The aim of this paper is to visualize the history of evidence-based medicine (EBM) and to examine the characteristics of EBM development in China and the West. We searched the Web of Science and the Chinese National Knowledge Infrastructure database for papers related to EBM. We applied information visualization techniques, citation analysis, cocitation analysis, cocitation cluster analysis, and network analysis to construct historiographies, themes networks, and chronological theme maps regarding EBM in China and the West. EBM appeared to develop in 4 stages: incubation (1972-1992 in the West vs. 1982-1999 in China), initiation (1992-1993 vs. 1999-2000), rapid development (1993-2000 vs. 2000-2004), and stable distribution (2000 onwards vs. 2004 onwards). Although there was a lag in EBM initiation in China compared with the West, the pace of development appeared similar. Our study shows that important differences exist in research themes, domain structures, and development depth, and in the speed of adoption between China and the West. In the West, efforts in EBM have shifted from education to practice, and from the quality of evidence to its translation. In China, there was a similar shift from education to practice, and from production of evidence to its translation. In addition, this concept has diffused to other healthcare areas, leading to the development of evidence-based traditional Chinese medicine, evidence-based nursing, and evidence-based policy making.

Source

Journal of the American Society for Information Science and Technology. 64(2013) no.10, S.2157-2172

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Abstract

Text mining has been widely used in multiple types of user-generated data to infer user opinion, but its application to microblogging is difficult because text messages are short and noisy, providing limited information about user opinion. Given that microblogging users communicate with each other to form a social network, we hypothesize that user opinion is influenced by its neighbors in the network. In this paper, we infer user opinion on a topic by combining two factors: the user's historical opinion about relevant topics and opinion influence from his/her neighbors. We thus build a topic-level opinion influence model (TOIM) by integrating both topic factor and opinion influence factor into a unified probabilistic model. We evaluate our model in one of the largest microblogging sites in China, Tencent Weibo, and the experiments show that TOIM outperforms baseline methods in opinion inference accuracy. Moreover, incorporating indirect influence further improves inference recall and f1-measure. Finally, we demonstrate some useful applications of TOIM in analyzing users' behaviors in Tencent Weibo.

Source

Journal of the Association for Information Science and Technology. 66(2015) no.12, S.2657-2673

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Abstract

Information from microblogs is gaining increasing attention from researchers interested in analyzing fluctuations in stock markets. Behavioral financial theory draws on social psychology to explain some of the irrational behaviors associated with financial decisions to help explain some of the fluctuations. In this study we argue that social media users who demonstrate an interest in finance can offer insights into ways in which irrational behaviors may affect a stock market. To test this, we analyzed all the data collected over a 3-month period in 2011 from Tencent Weibo (one of the largest microblogging websites in China). We designed a social influence (SI)-based Tencent finance-related moods model to simulate investors' irrational behaviors, and designed a Tencent Moods-based Stock Trend Analysis (TM_STA) model to detect correlations between Tencent moods and the Hushen-300 index (one of the most important financial indexes in China). Experimental results show that the proposed method can help explain the data fluctuation. The findings support the existing behavioral financial theory, and can help to understand short-term rises and falls in a stock market. We use behavioral financial theory to further explain our findings, and to propose a trading model to verify the proposed model.

Source

Journal of the Association for Information Science and Technology. 70(2019) no.9, S.1000-1013

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Source

Journal of the Association for Information Science and Technology. 68(2017) no.7, S.1737-1750

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Source

Journal of the American Society for Information Science and Technology. 62(2011) no.9, S.1849-1866

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Source

Journal of the American Society for Information Science and Technology. 63(2012) no.1, S.139-162